July 2022 - talks - lists.cs.princeton.edu

CS Colloquium Series: week of April 11-15
by Emily C. Lawrence 19 Sep '22

19 Sep '22

Here is the full list of CS Colloquium talks for next week. All talks will be recorded. ~~~~~ CS Colloquium Speaker Speaker: Parastoo Abtahi, Stanford University Date: Monday, April 11, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Andrés Monroy-Hernández & Adam Finkelstein Event page: [ https://www.cs.princeton.edu/events/26186 | https://www.cs.princeton.edu/events/26186 ] This talk will be live-streamed at [ https://mediacentrallive.princeton.edu/ | https://mediacentrallive.princeton.edu/ ] Title: From Haptic Illusions in Virtual Reality to Beyond-Real Interactions Abstract: Advances in audiovisual rendering have led to the commercialization of virtual reality (VR) hardware; however, haptic technology has not kept up with these advances. While haptic devices aim to bridge this gap by simulating the sensation of touch, there are many hardware limitations that make realistic touch interactions in VR challenging. In my research, I explore how by understanding human perception, we can design VR interactions that not only overcome the current limitations of VR hardware, but also extend our abilities beyond what is possible in the real world. In this talk, I will present my work on redirection illusions that leverage the limits of human perception to improve the perceived performance of encountered-type haptic devices, such as improving the position accuracy of drones, the speed of tabletop robots, and the resolution of shape displays when used for haptics in VR. I will then present a framework I have developed through the lens of sensorimotor control theory to argue for the exploration and evaluation of VR interactions that go beyond mimicking reality. Bio: Parastoo Abtahi is a final year computer science PhD candidate and a Gerald J. Lieberman fellow at Stanford University, where she is co-advised by Prof. James Landay and Prof. Sean Follmer. Her research area is human-computer interaction (HCI) and she works broadly on virtual reality interactions and spatial computing. Her research has been published at top HCI venues, such as the ACM Conference on Human Factors in Computing Systems (CHI) and the ACM User Interface Software and Technology Symposium (UIST), and has received two honorable mention paper awards. Her work has been supported by the Stanford Institute for Human-Centered Artificial Intelligence, the Hasso Plattner Design Thinking Research Program, and the VMware fellowship. Prior to Stanford, Parastoo received her bachelor’s degree in Electrical and Computer Engineering from the University of Toronto as part of the Engineering Science program. ~~~~~ CS Colloquium Speaker Speaker: Ferran Alet, Massachusetts Institute of Technology Date: Tuesday, April 12, 2022 Time: 12:30pm EST Location: CS 105 Host: Olga Russakovsky Event page: https://www.cs.princeton.edu/events/26187 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: A flexible framework for machine learning Abstract: In this last decade, we have seen a lot of progress in AI and machine learning using a single recipe: given a task, we train a single neural network to map inputs to outputs. In this talk, I will show that this one-neural-network-per-task framework can be extended to improve generalization. First, I will describe modular meta-learning, which achieves language-like generalization by training a set of composable neural modules. By having multiple neural networks per task, and multiple tasks per neural network, we are able to reuse information and achieve bigger data and computational efficiency. In the second part of my talk, I will describe tailoring, a general way of encoding inductive biases in neural networks by optimizing unsupervised objectives inside the prediction function, essentially having one neural network per input. Finally, I will describe my vision for creating a flexible ML framework that will enable training reinforcement learning policies within minutes rather than days, solving complex search and discovery problems, and improving our understanding of generalization in deep learning. Bio: Ferran Alet is a PhD candidate at MIT CSAIL advised by Leslie Kaelbling, Tomas Lozano-Perez, and Joshua Tenenbaum. His research is on machine learning and leverages techniques from meta-learning, learning to search, program synthesis, and insights from mathematics and the physical sciences. During his PhD, he created the MIT Embodied Intelligence Seminar, mentored 17 students, and won the MIT Outstanding Mentor award 2021. Ferran studied mathematics and physics in Barcelona thanks to CFIS, a program for doing two degrees, where he was the valedictorian of his promotion. In college, he participated in the ACM-ICPC programming contest, being the most decorated in the history of his regional phase (South Western Europe). In grad school, he earned a “La Caixa” fellowship and was responsible for the high-level planner of the MIT-Princeton team for the Amazon Robotics Challenge, which won the stowing task in 2017. You can find more information and papers at www.alet-et.al ~~~~~ CS Colloquium Speaker Speaker: Benedikt Bünz, Stanford University Date: Thursday, April 14, 2022 Time: 12:30pm EST Location: CS 105 Hosts: Jonathan Mayer & Arvind Narayanan Event page: https://www.cs.princeton.edu/events/26188 This talk will be live-streamed at https://mediacentrallive.princeton.edu/ Title: Improving the privacy, scalability, and ecological impact of blockchains Abstract: Blockchains are an exciting area of research that touches on many areas of Computer Science and beyond. This technology has the potential to enable a fast, cheap, and private financial system based on distributed consensus and cryptography, instead of trusted parties. Despite this potential, the reality still shows severe limitations of blockchains: (i) transactions can cost hundreds of dollars and take minutes to confirm, (ii) some blockchains offer little privacy, and (iii) proof-of-work consensus consumes too much energy. In this talk, I will discuss powerful techniques that follow a prover paradigm and can mitigate these limitations. The first technique, called Bulletproofs, is a general-purpose zero-knowledge proof system that is specifically designed to enable confidential blockchain transactions. Bulletproofs requires minimal trust assumptions and gives the shortest zero-knowledge proofs without trusted setup. The system is widely deployed and powers tens of thousands of private blockchain transactions per day. The second technique, called inner pairing products, is a way to aggregate many zero-knowledge proofs into a single short proof. This can significantly reduce on-chain data, leading to a significant increase in transactions per second that the chain can process. The third technique is a new concept called a verifiable delay function (VDF) that is vital for permission-less and eco-friendly consensus. VDFs are already deployed in Filecoin and Chia, and are planned for Ethereum 2.0, the upcoming upgrade to Ethereum. Bio: Benedikt Bünz is a PhD candidate at Stanford University, a member of Dan Boneh’s applied cryptography lab, and a recipient of the Microsoft Research Fellowship at the Simons Institute. His work on the science of Blockchains uses tools from applied cryptography, distributed systems, and algorithmic game theory. His research focuses on building new proof protocols for improving the privacy, scalability, and ecological impact of blockchains. Several of his research results have had a significant industry impact. His work on Bulletproofs, secures tens of thousands of private transactions on Blockchains like Monero or Signal’s Mobilecoin. His seminal work on Verifiable Delay Functions (VDFs) sparked the VDF Alliance, a multi-million dollar initiative composed of academic, non-profit, and corporate collaborators.

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Seminar Series on Security & Privacy in Machine Learning
by Emily C. Lawrence 02 Aug '22

02 Aug '22

Challenges and Opportunities in Security & Privacy in Machine Learning Weekly talks starting Tuesday, June 7 About the seminar series The motivation for the seminar is to build a platform to discuss and disseminate the progress made by the community in solving some of the core challenges. We intend to host weekly talks from leading researchers in both academia and industry. Each session will be split into a talk (40 mins) followed by a Q&A + short discussion session (20 mins). Timing : Every Tuesday at 1pm Eastern Time (Virtual talks) Website: [ https://vsehwag.github.io/SPML_seminar/ | https://vsehwag.github.io/SPML_seminar/ ] Mailing list: [ https://groups.google.com/forum/#!forum/spml-seminars/join | Link to mailing list ] Calendar: [ https://calendar.google.com/calendar/u/0?cid=N2FwbTVxYzJsOGM2bXBiNGY4am1oMj… | Link to calendar ] You can find all additional details on the website. If you are interested, we recommend signing up for the mailing list and sync the calendar to stay up to date with the seminar schedule. -- Vikash Sehwag PhD Candidate Princeton University [ https://vsehwag.github.io/ | https://vsehwag.github.io ]

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Princeton AI Club: Dr. Naila Murray from Facebook AI Research, August 1st 2022, at 11 AM ET
by Emily C. Lawrence 22 Jul '22

22 Jul '22

Reminder for our next guest speaker! The Princeton AI Club is excited to announce its next guest speaker – [ https://twitter.com/nailamurray | Dr. Naila Murray ] from Facebook AI Research! Her talk will focus on how to train deep vision models in low-data regimes If you want to hear all about how to train your network with little data, sign-up for her virtual talk [ https://forms.gle/fThq5gxa6Yzx1dkh8 | here ] , and join us on August 1 st , at 11 am Eastern Time (ET) ! The full details are below. Speaker: Dr. Naila Murray (Facebook AI Research) Title: Training deep vision models in low-data regimes Date: Monday, August 1st 2022, 11:00 AM ET Abstract: Large-scale vision models, trained on vast quantities of often unlabelled data, give state-of-the-art performance when fine-tuned for a wide variety of downstream tasks. For these downstream tasks, the amount of training data available is usually orders of magnitude less than what was used during pre-training. And in many cases, there is little to no labelled data available. In this talk, I discuss approaches to fine-tuning pre-trained models for vision tasks in low-data regimes. In all cases, which cover retrieval, video segmentation, and object detection, I show that incorporating domain-specific and modality-specific inductive biases lead to improved model performance when training data is limited. Bio: Naila Murray obtained a BSE in electrical engineering from Princeton University in 2007. In 2012, she received her Ph.D. from the Universitat Autonoma de Barcelona, in affiliation with the Computer Vision Center. She joined Xerox Research Centre Europe in 2013 as a research scientist in the computer vision team, working on topics including fine-grained visual categorization, image retrieval and visual attention. From 2015 to 2019 she led the computer vision team at Xerox Research Centre Europe, and continued to serve in this role after its acquisition and transition to becoming NAVER LABS Europe. In 2019, she became the director of science at NAVER LABS Europe. In 2020, she joined Facebook AI Research where she is a senior research engineering manager for EMEA. She has served as area chair for ICLR 2018, ICCV 2019, ICLR 2019, CVPR 2020, ECCV 2020, and program chair for ICLR 2021. Her current research interests include few-shot learning and domain adaptation.

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Kaiyu Yang will present his FPO "Neurosymbolic Machine Learning for Reasoning" on Thursday, July 28, 2022 at 3:00 PM in Friend 125 and Zoom.
by Louis W. Riehl 21 Jul '22

21 Jul '22

Kaiyu Yang will present his FPO "Neurosymbolic Machine Learning for Reasoning" on Thursday, July 28, 2022 at 3:00 PM in Friend 125 and Zoom. Location: Zoom link: [ https://princeton.zoom.us/j/2731344683 | https://princeton.zoom.us/j/2731344683 ] The members of Kaiyu’s committee are as follows: Examiners: Jia Deng (Adviser), Olga Russakovsky, Danqi Chen Readers: Karthik Narasimhan, Mayur Naik (University of Pennsylvania) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Machine learning has made significant progress in the past decade. Its most successful paradigm, deep neural networks, consists of layers of continuous representation whose parameters are optimized on massive datasets via gradient descent. Deep neural networks have achieved superior performance on numerous tasks, such as object recognition, language understanding, and autonomous driving. However, they still struggle with reasoning tasks, which often require manipulating symbols and chaining multiple steps compositionally, e.g., solving math equations or writing computer programs. In this dissertation, we aim to bridge this gap and teach machines to reason—in ways that are precise, systematic, interpretable, and robust to ambiguity in real-world environments. To that end, we take a neurosymbolic approach combining the complementary strengths of machine learning and symbolic reasoning. Symbolic reasoning is precise and generalizes systematically. But it has been restricted to domains amenable to rigid formalization. In contrast, predominant machine learning methods are flexible but notoriously uninterpretable, data-hungry, and incapable of generalizing outside the training distribution. Integrating the strengths of both approaches is essential for building flexible reasoning machines with precise and systematic generalization. Concretely, this dissertation addresses neurosymbolic reasoning from two angles. First, we apply machine learning to tasks related to symbolic reasoning, such as automated theorem proving (Chapter 2). Second, we introduce inductive biases inspired by symbolic reasoning into machine learning models to improve their interpretability, generalization, and data efficiency (Chapter 3 and Chapter 4). Our results highlight the benefits of (1) neurosymbolic model architectures, (2) reasoning at a suitable level of abstraction, and (3) an explicit, compositional representation of reasoning, such as symbolic proofs. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Xingyuan Sun will present his Pre-FPO "Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects" on Thursday, July 28, 2022 at 11:00 AM in CS 402 and Zoom.
by Louis W. Riehl 21 Jul '22

21 Jul '22

Xingyuan Sun will present his Pre-FPO "Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects" on Thursday, July 28, 2022 at 11:00 AM in CS 402 and Zoom. Zoom link: [ https://princeton.zoom.us/j/95907350060 | https://princeton.zoom.us/j/95907350060 ] Committee Members: Ryan P. Adams (Examiner, adviser), Szymon Rusinkiewicz (Examiner, adviser), Olga Russakovsky (Examiner), Sigrid Adriaenssens (reader), Felix Heide (reader). All are welcome to attend. Title: Using Machine Learning to Improve Fiber-Reinforced 3D Printed Objects Abstract: This dissertation studies fiber layout planning given a shape and external loads using machine learning, improving the object’s stiffness. However, simply planning fiber paths is not sufficient. Since fibers are stiff, their shape is smoothed after extrusion. We thus need another planning algorithm to generate an extruder path given any desired fiber path. To solve this task, we learn a two-stage neural network that we may consider an autoencoder. A decoder is first learned as a differentiable surrogate of the printing process, taking an extruder path and producing a resulting fiber path. An encoder is then trained to produce an extruder path given a desired fiber path, using the trained decoder to evaluate the quality of the planned extruder path. We compare this approach with direct supervised learning of the extruder path planning problem and direct optimization of the extruder path. Besides, we also study our algorithm in another case study on a soft robot, showing our approach can be applied to design problems with a similar structure. Results from simulation and real experiments show our algorithm generates higher quality solutions than direct supervised learning and solutions with competitive quality with direct optimization but greatly reduced running time. To plan fiber paths, we formalize the task as an optimization problem, solving the fiber layout to maximize the object’s stiffness with regularizers forcing paths to be feasible. We initialize every fiber path by iteratively using finite element analysis to calculate the stress field of the object and a greedy algorithm to “walk” on the field. We then use the adjoint method to calculate the gradient of the regularized stiffness with respect to the fiber layout, and optimize the layout using a gradient-based optimizer. We compare our algorithm with three baselines, including concentric fiber rings generated by Eiger, a leading digital manufacturing software, and two others producing greedy fiber paths on the stress field and a fiber orientation field calculated by smoothing the stress field, respectively. Experiments on simulation and real printings show objects with fiber paths from our algorithm improve the Pareto front, achieving larger stiffness while using less fiber. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Zoe Ashwood will present her FPO "Probabilistic Models for Characterizing Animal Learning and Decision-Making" on Monday, July 25, 2022 at 9:00 AM in PNI 101 and Zoom.
by Louis W. Riehl 18 Jul '22

18 Jul '22

Zoe Ashwood will present her FPO "Probabilistic Models for Characterizing Animal Learning and Decision-Making" on Monday, July 25, 2022 at 9:00 AM in PNI 101 and Zoom. Location: PNI 101 Zoom link: [ https://princeton.zoom.us/j/93691725152?pwd=RlpLWkRObG9lMWs5cTAxZTRRSFVkZz09 | https://princeton.zoom.us/j/93691725152?pwd=RlpLWkRObG9lMWs5cTAxZTRRSFVkZz09 ] The members of Zoe’s committee are as follows: Examiners: Jonathan Pillow (Adviser), Ryan Adams, Tom Griffiths Readers: Barbara Engelhardt, Alexandre Pouget (University of Geneva) A copy of her thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend her talk. Abstract follows below: A central goal in neuroscience is to identify the strategies used by animals and humans as they make decisions, and to characterize the learning rules that shape these policies in the first place. In this thesis, I discuss three projects aimed at tackling this goal. In the first, we introduce a novel framework, the GLM-HMM, for characterizing mouse and human choice policies during binary decision-making tasks. The GLM-HMM is a hidden Markov model with Bernoulli Generalized Linear Model observations. By fitting the GLM-HMM to hundreds of thousands of decisions, our framework revealed that — contrary to common wisdom — mice and humans use multiple strategies over the course of a single session to perform perceptual decision-making tasks. In the second project, we sought to uncover the learning rules used by mice and rats as they learned to perform this type of task. Our model tracked trial-to-trial changes in the animals’ choice policies, and separated these changes into components explainable by a reinforcement learning rule, and components that remained unexplained. Whereas the average contribution of the conventional REINFORCE learning rule to the policy update for mice learning a common task was just 30%, we found that adding baseline parameters allowed the learning rule to explain 92% of the animals’ policy updates under our model. Finally, I discuss our approach to applying inverse reinforcement learning (IRL) to the trajectories of mice exploring a maze environment. While IRL has been widely applied in robotics and healthcare settings to infer the unknown reward function of an agent, it has yet to be applied extensively in neuroscience. One potential reason for this is that existing IRL frameworks assume that an agent’s reward function is fixed over time. To overcome this limitation, we introduce ‘DIRL’, an IRL framework that allows for time-varying intrinsic rewards. Our method returns interpretable reward functions for two separate cohorts of mice, and provides a novel characterization of exploratory behavior. Overall, we anticipate ‘DIRL’ having broad applicability in neuroscience, and that it could also facilitate the design of biologically-inspired reward functions for training artificial agents to perform analogous tasks. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Claudia Roberts will present her Pre FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, July 14 2:00pm ET via Zoom.
by Nicki Mahler 12 Jul '22

12 Jul '22

Claudia Roberts will present her Pre FPO "Human-machine Cooperation in Machine-Learning Resistant Real-World Applications" on Thursday, July 14 2:00pm ET via Zoom. Zoom link: [ https://princeton.zoom.us/j/2202514985 | https://princeton.zoom.us/j/2202514985 ] Advisor: Arvind Narayanan (examiner) Committee members: Adji Bousso Dieng (reader), Andrés Monroy-Hernández (examiner), Barbara Engelhardt (reader), Matt Salganik (examiner) Title: Human-machine Cooperation in Machine-Learning Resistant Real-World Applications All are welcome to attend. Abstract: Automation tools like machine learning are a necessity in our big data world. However, not all applications lend themselves to machine-learning based automation. Numerous societal problems emerge when we attempt to measure and quantify something that resists measurement and subsequently, develop mathematical intermediaries to attempt to model human behavior. However, using machine-learning backed automation to reduce the scale of many real-world applications is still helpful. In this dissertation, we explore three different ways in which we can use human-machine cooperation in machine-learning resistant applications to keep or increase utility and reduce harm when using these systems in the wild. The first chapter, based on “Friend Request Pending: A Comparative Assessment of Engineering and Social Science Inspired Approaches to Analyzing Complex Birth Cohort Survey Data,” shows how the first mode of human-machine cooperation—explaining things to computers by adding domain knowledge—increases utility of machine learning models in the machine-learning resistant task of grade point average (GPA) prediction. The Fragile Families Challenge is a mass collaboration social science data challenge whose aim is to learn how various early childhood variables predict the long-term outcomes of children. We describe our two-step approach to the Fragile Families Challenge. In step 1, we use a variety of fully automated approaches to predict child academic achievement. We fit 124 models, which involve most possible combinations of 8 model types, 2 imputation strategies, 2 standardization approaches, and 2 automatic variable selection techniques using 2 different thresholds. Then, in step 2, we attempt to improve on the results from step 1 with manual variable selection based on a detailed review of the codebooks. We manually selected 3,694 variables believed to be predictive of academic achievement, using a comprehensive review of student success literature to guide the decision-making process. The best models from step 1 were re-estimated using the manually selected variables. We show that manual variable selection improved the majority of the top 10 models in step 1, but did not improve the best of the top 10. Results indicate that variable selection inspired by social science methodologies can, in most cases, significantly improve models trained completely automatically. The second chapter, based on “Selectively Contextual Bandits,” shows how the second mode of human-machine cooperation—humans and computers working together in decision making—maintains utility while reducing harm of machine learning models in the machine-learning resistant task of image personalization. Personalization is an integral part of most web-service applications and determines which experience to display to each member. A popular algorithmic framework used in industrial personalization systems are contextual bandits, which seek to learn a personalized treatment assignment policy in the presence of treatment effects that vary with the observed contextual features of the members. In order to keep the optimization task tractable, such systems can myopically make independent personalization decisions that can conspire to create a suboptimal experience in the aggregate of the member’s interaction with the web-service. We design a new family of online learning algorithms that benefit from personalization while optimizing the aggregate impact of the many independent decisions. Our approach selectively interpolates between any contextual bandit algorithm and any context-free multi-armed bandit algorithm and leverages the contextual information for a treatment decision only if this information promises significant gains over a decision that does not take it into account. Apart from helping users of personalization systems feel less targeted, simplifying the treatment assignment policy by making it selectively reliant on the context can help improve the rate of learning. We evaluate our approach on several datasets including a video subscription web-service and show the benefits of such a hybrid policy. Finally, the last chapter, based on “On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks,” shows how the third mode of human-machine cooperation—computers explaining to humans what it’s thinking—can improve utility and reduce harm of machine learning models in the machine-learning resistant task of movie recommendations. We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set. LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIMB) that is superior to LIME and much faster than SHAP.

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Princeton AI Club Seminar Series: Yoshua Bengio, July 14 at 4pm
by Emily C. Lawrence 08 Jul '22

08 Jul '22

Princeton AI Club Seminar Speaker : Prof. Yoshua Bengio Title : GFlowNets and AI for Science Date : Thursday, July 14 at 4:00 PM ET Location : Zoom (register to receive the link) Registration : [ https://forms.gle/iFoKEty2BVmp3nKQ9 | https://forms.gle/iFoKEty2BVmp3nKQ9 ] For more info : [ https://aiclub.princeton.edu/seminars/ | https://aiclub.princeton.edu/seminars/ ] Abstract : Machine learning research is expanding its reach, beyond the traditional realm of the tech industry and into the activities of other scientists, opening the door to truly transformative advances in these disciplines. In this talk I will focus on two aspects, modeling and experimental design, that are intertwined in the theory-experiment-analysis active learning loop that constitutes a core element of the scientific methodology. Computers will be necessary to go beyond the currently purely manual research loop and take advantage of high-throughput experimental setups and large-scale experimental datasets. I will introduce a novel machine learning framework called GFlowNets (for “Generative Flow Networks”), related to reinforcement learning, generative modeling and variational methods and conceived as an ML-driven replacement for MCMC. GFlowNets were first used to propose a highly diverse set of molecular candidates and were then incorporated in an active learning framework for efficiently looking for molecules with desirable properties. More recently, we have been exploring how GFlowNets can generate not just molecular graphs but also causal graphs and Bayesian posterior distributions in function space. I will describe our research program to build on these bases and develop machine learning methodologies for efficiently exploring the space of causal theories as well as the space of experiments while characterizing the ambiguities left by finite datasets and non-identifiability, as well as our plans to apply these tools in areas of great societal need like the unmet challenge of antimicrobial resistance. Bio : Recognized worldwide as one of the leading experts in artificial intelligence, Yoshua Bengio is most known for his pioneering work in deep learning, earning him the 2018 A.M. Turing Award, “the Nobel Prize of Computing,” with Geoffrey Hinton and Yann LeCun. He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO. In 2019, he was awarded the prestigious Killam Prize and in 2021, became the second most cited computer scientist in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada. Concerned about the social impact of AI and the objective that AI benefits all, he actively contributed to the Montreal Declaration for the Responsible Development of Artificial Intelligence.

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Zachary Teed will present his FPO "Optimization Inspired Neural Networks for Multiview 3D Reconstruction" on Friday, July 8, 2022 at 3:00 PM in Friend 202.
by Louis W. Riehl 01 Jul '22

01 Jul '22

Zachary Teed will present his FPO "Optimization Inspired Neural Networks for Multiview 3D Reconstruction" on Friday, July 8, 2022 at 3:00 PM in Friend 202. Location: Friend Center 202 The members of Zachary’s committee are as follows: Examiners: Jia Deng (Adviser), Adam Finkelstein, Felix Heide Readers: Szymon Rusinkiewicz, Kilian Weinberger (Cornell University) A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: 3D reconstruction is usually formulated as an optimization problem. We define a measure of solution quality (objective) and design a search algorithm for finding good solutions (optimizer). Both problems are challenging. The objective needs to adequately capture the complexity of the 3D world while simultaneously being simple enough such that it is tractable using standard techniques. This is in contrast to deep learning where inference is performed using a neural network with learned weights. Despite the success of neural networks on semantic tasks their performance on multiview reconstruction tasks is often less accurate and less generalizable than approaches based purely in optimization. In this thesis, we show that optimization algorithms can be learned from data and used for motion estimation and reconstruction. We design a generic network architecture with interleaves interative updates with domain specific implicit layers. We show that such networks can automate both modeling (objective function design) and optimization. We apply this approach to a three representative multiview problems: optical flow, scene flow, and simultaneous localization and mapping. We push forward performance on these problems while using a general design strategy. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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