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

Ankit Goyal will present his FPO "Towards Geometric Intelligence" on Monday, August 15, 2022 at 10:00 AM in Friend Center 202 and Zoom.
by Louis W. Riehl 08 Aug '22

08 Aug '22

Ankit Goyal will present his FPO "Towards Geometric Intelligence" on Monday, August 15, 2022 at 10:00 AM in Friend Center 202 and Zoom. Location: Zoom link: https://princeton.zoom.us/my/agoyal The members of Ankit’s committee are as follows: Examiners: Jia Deng (Adviser), Szymon Rusinkiewicz, Danqi Chen Readers: Karthik Narasimhan, Vladlen Koltun (Apple) 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: Geometric intelligence is the aspect of human intelligence that relates to perceiving, communicating and reasoning about geometries. Artificial agents like robots must possess geometric intelligence to operate in unconstrained human environments and collaborate with humans in day-to-day life. Geometric intelligence is an umbrella term that encompasses many abilities. In this thesis, we focus on three crucial abilities – first, the ability to see or perceive geometry; second, the ability to communicate about geometry and space and third, the ability to reason and plan about geometries. For studying the ability to perceive geometry, we pursue two efforts. In one effort, we build a system, called IFOR that recognizes the geometric difference between two scenes and rearrange one scene into another. IFOR can handle unseen objects and transfer to the real world while being trained only on synthetic data. In another effort, we revisit the literature on perceiving objects from point clouds and uncover two surprising results. First, we show that auxiliary factors that are independent of network architecture explain most of the performance improvement. Second, we show that a simple view-based baseline outperforms sophisticated state-of-the-art methods. For studying the ability to communicate about geometry, we focus on recognizing spatial relations, which are the atomic elements used to communicate about geometric arrangements. We find that existing datasets are insufficient as they lack largescale, high-quality 3D ground truth information, which is critical for learning spatial relations. We fill this gap by constructing Rel3D. Finally, for studying the ability to reason about geometry, we pursue two efforts. In one effort, we study the problem of geometric reasoning in the context of question-answering. We introduce Dynamic Spatial Memory Network (DSMN), a new deep network architecture designed for answering questions that admit latent visual representations. In another effort, we explore the problem of geometric planning which requires simultaneous reasoning about geometries and planning. We find that existing benchmarks are insufficient for the problem. We propose PackIt – a virtual environment for geometric planning. We benchmark various baselines on PackIt and find that learning could be a viable way to gain geometric planning skills. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Princeton AI Club speaker: Prof. Max Welling, August 15th at 10 AM ET
by Emily C. Lawrence 04 Aug '22

04 Aug '22

The Princeton AI Club is thrilled to announce its next guest speaker – [ https://staff.fnwi.uva.nl/m.welling/ | Max Welling ] from the University of Amsterdam! His talk will focus on how we can apply machine learning to scientific discovery If you want to about new opportunities to apply machine learning to scientific discovery, sign-up for his virtual talk [ https://forms.gle/ms5hewGsMc8SUgaL7 | here ] , and join us on August 15 th , at 10 am Eastern Time (ET) ! The full details are below. Speaker: Prof. Max Welling (University of Amsterdam) Title: Deep Learning and the Natural Sciences: A Perfect Marriage? Date: Monday, August 15 at 4:00PM CEST (which is 10:00 AM EST.) Abstract: Artificial Intelligence has always had a deep connection with the natural sciences. Artificial neural networks were first conceived as abstract ions of biological neural networks, as do many subsequent algorithms such as reinforcement learning. Neural networks have also been inspired by many concepts in physics, for example Hopfield networks are modelled after Ising models. More recently, concepts from physics such as symmetries and the renormalization group have also been incorporated in the design of novel deep architectures. With these new tools, deep learning has completely revolutionized fields such as Natural Language Processing, and Computer Vision. Also in the reverse direction, machine learning has contributed tremendously to the analysis of data from experiments, such as particle collision events at CERN. However, recently, a new scientific paradigm is emerging where deep learning models are accelerating and improving scientific simulations, dubbed by some the fifth scientific paradigm. In this talk I will first give an introduction to the opportunities that lie ahead of us to apply machine learning to scientific discovery. In the second half of the talk, I will talk in more technical detail about symmetries in deep learning and our latest ideas on how to model the hidden layers of a deep neural network as a field described by a partial differential equation. Amazingly, this field-view of the hidden layers of an artificial cortex can be extended to a quantum field and mapped to an optical quantum computer design. How useful this intellectual exercise is in terms of practical quantum computers I will leave to the imagination of the audience. Bio: Prof. Dr. Max Welling is a research chair in Machine Learning at the University of Amsterdam and a Distinguished Scientist at MSR. He is a fellow at the Canadian Institute for Advanced Research (CIFAR) and the European Lab for Learning and Intelligent Systems (ELLIS) where he also serves on the founding board. His previous appointm z ents include VP at Qualcomm Technologies, professor at UC Irvine, postdoc at U. Toronto and UCL under supervision of prof. Geoffrey Hinton, and postdoc at Caltech under supervision of prof. Pietro Perona. He finished his PhD in theoretical high energy physics under supervision of Nobel laureate prof. Gerard ‘t Hooft. Max Welling has served as associate editor in chief of IEEE TPAMI from 2011-2015, he serves on the advisory board of the Neurips foundation since 2015 and has been program chair and general chair of Neurips in 2013 and 2014 respectively. He was also program chair of AISTATS in 2009 and ECCV in 2016 and general chair of MIDL 2018. Max Welling is recipient of the ECCV Koenderink Prize in 2010 and the ICML Test of Time award in 2021. He directs the Amsterdam Machine Learning Lab (AMLAB) and co-directs the Qualcomm-UvA deep learning lab (QUVA) and the Bosch-UvA Deep Learning lab (DELTA).

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Zhuqi Li will present his Pre FPO "Cross-layer optimization for video delivery on Wireless Networks" on Wednesday, August 10, 2022 at 3pm (EST) via Zoom.
by Nicki Mahler 03 Aug '22

03 Aug '22

Zhuqi Li will present his Pre FPO "Cross-layer optimization for video delivery on Wireless Networks" on Wednesday, August 10, 2022 at 3pm (EST) via Zoom. Zoom link: [ https://princeton.zoom.us/j/6871275896 | https://princeton.zoom.us/j/6871275896 ] The members of his committee are as follows: Advisor: Kyle Jamieson Readers: Victor Bahl (Microsoft), Amit Levy Examiners: Ravi Netravali, Jennifer Rexford All are welcome to attend. Abstract: A dominating portion of today's wireless network traffic is occupied by video content delivery for both video streaming and live video analytic purposes. The dynamic nature of wireless network presents significant challenges to both applications. On the one hand, the available throughput of the wireless network varies across time due to the change of the wireless channel and competing flows. On the other hand, the endpoints need to decide the video bitrate and routing selection without knowing the future throughput. In this talk, we will introduce three systems that optimize wireless video delivery from physical layer, network layer, and application layer to solve the challenges. We present the design and implementation of the following systems: (1) LAIA: a programmable wireless environment that allows rapid adjustments of the wireless channel to smooth the throughput variation. LAIA uses a large array of inexpensive antenna (LAIA) elements and includes algorithms to configure LAIA elements in real-time. (2) Spider: a multi-hop, millimeter-wave (mmWave) wireless relay network design to maximize end-to-end video analytic accuracy for a large camera cluster. Spider integrates a low-latency Wi-Fi control plane with a mmWave relay data plane, allowing agile re-routing around blockages. Spider proposes a novel video bit-rate allocation algorithm coupled with a scalable routing algorithm that works hand-in-hand toward the application-level objective of maximizing video analytics accuracy, rather than simply maximizing data throughput. (3) Dashlet: a system tailored for the high quality of experience in short video streaming applications. Dashlet proposes a novel out-of-order video chunk pre-buffering mechanism that leverages users’ swipe statistics as well as network throughput history to determine the pre-buffering order and bitrate.

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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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Happening today! Our next guest speaker is Dr. Naila Murray from Facebook AI Research!
by Emily C. Lawrence 01 Aug '22

01 Aug '22

Happening today! 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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