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

Ishita Chaturvedi will present her General Exam "GPU Out-Of-Order Processing" on Wednesday, April 20, 2022 at 1:00 PM in Friend 202 and via zoom.
by Louis W. Riehl 14 Apr '22

14 Apr '22

Ishita Chaturvedi will present her General Exam "GPU Out-Of-Order Processing" on Wednesday, April 20, 2022 at 1:00 PM in Friend 202 and via zoom. Zoom link: [ https://princeton.zoom.us/my/ishita.c | https://princeton.zoom.us/my/ishita.c ] Committee Members: David August (advisor), David Wentzlaff, Tyler Sorensen Abstract: GPUs have become ubiquitous today for high-performance computing. While GPUs leverage massive multithreaded parallelism to obtain speedup, they still suffer from having multiple stall cycles. While some stall reasons can be fixed others must be tolerated. We introduce the Def-Ex GPU infrastructure which improves GPU performance using Out-of-Order instruction execution. We focus on the Deferred Execution Buffer which defers the execution of stalling instructions to issue them later. This gives an opportunity for the next instructions in the pipeline to execute, thus reducing the stall cycles. We see a maximum speedup of 26% and a geomean speedup of 14% on the Rodinia benchmark suite with the Deferred Execution Buffer. Reading List: [ https://docs.google.com/document/d/1LxXAI99yrbRsGa6kc56JCsnmBwQ8nzs3B2E9pTF… | https://docs.google.com/document/d/1LxXAI99yrbRsGa6kc56JCsnmBwQ8nzs3B2E9pTF… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Huihan Li will present her MSE talk "Re-evaluating Conversational Question Answering" on April 21, 3:30pm via Zoom
by Nicki Mahler 14 Apr '22

14 Apr '22

Huihan Li will present her MSE talk "Re-evaluating Conversational Question Answering" on April 21, 3:30pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/92763452837 | https://princeton.zoom.us/j/92763452837 ] Huihan's committee is as follows: Danqi Chen (adviser) and Karthik Narasimhan (reader) All are welcome to attend. Abstract: Conversational question answering aims to provide natural-language answers to users ininformation-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human- machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human-machine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and gold-history evaluation in terms of model ranking. We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we demonstrate that training conversational question answering systems using their own predictions benefits the models under the new automatic evaluation protocol.

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Mengzhou Xia will present her General Exam "Structured Pruning Learns Compact and Accurate Models" on Tuesday, April 19, 2022 at 1:00 PM in CS 302 and via Zoom.
by Louis W. Riehl 12 Apr '22

12 Apr '22

Mengzhou Xia will present her General Exam "Structured Pruning Learns Compact and Accurate Models" on Tuesday, April 19, 2022 at 1:00 PM in CS 302 and via Zoom. Zoom link: [ https://princeton.zoom.us/j/6256370935 | https://princeton.zoom.us/j/6256370935 ] Committee Members: Danqi Chen (advisor), Karthik Narasimhan, Kai Li Abstract: The growing size of neural language models has led to increased attention in model compression. The two predominant approaches are pruning, which gradually removes weights from a pre-trained model, and distillation, which trains a smaller compact model to match a larger one. Pruning methods can significantly reduce the model size but hardly achieve large speedups as distillation. However, distillation methods require large amounts of unlabeled data and are expensive to train. In this work, we propose a task-specific structured pruning method CoFi (Coarse- and Fine-grained Pruning), which delivers highly parallelizable subnetworks and matches the distillation methods in both accuracy and latency, without resorting to any unlabeled data. Our key insight is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads and hidden units) modules, which controls the pruning decision of each parameter with masks of different granularity. We also devise a layerwise distillation strategy to transfer knowledge from unpruned to pruned models during optimization. Our experiments on GLUE and SQuAD datasets show that CoFi yields models with over 10x speedups with a small accuracy drop, showing its effectiveness and efficiency compared to previous pruning and distillation approaches. Reading List: [ https://docs.google.com/document/d/1cJzfOnk7OEn1ssGHZlpheGDRCZ9I9bO4Lcdk3EF… | https://docs.google.com/document/d/1cJzfOnk7OEn1ssGHZlpheGDRCZ9I9bO4Lcdk3EF… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Samyak Gupta will present his General Exam on April 15th, 2022 at 1pm via Zoom.
by Nicki Mahler 12 Apr '22

12 Apr '22

Samyak Gupta will present his General Exam on April 15th, 2022 at 1pm via Zoom. Zoom link: [ https://princeton.zoom.us/my/samyakg | https://princeton.zoom.us/my/samyakg ] The members of his committee are as follows: Kai Li (adviser), Danqi Chen, and Jon Cohen (Neuroscience) Link to Abstract and Reading list: [ https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… | https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… ]

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Vishvak Murahari will present his General Exam "DataMUX: Data Multiplexing for Neural Networks" on Monday, April 18, 2022 at 3:30 pm in CS 302 and via Zoom.
by Louis W. Riehl 11 Apr '22

11 Apr '22

Vishvak Murahari will present his General Exam "DataMUX: Data Multiplexing for Neural Networks" on Monday, April 18, 2022 at 3:30 pm in CS 302 and via Zoom. Zoom Link: [ https://princeton.zoom.us/j/3829472199 | https://princeton.zoom.us/j/3829472199 ] Committee Members: Karthik Narasimhan (adviser), Danqi Chen, Sanjeev Arora Abstract: In this talk, we introduce data multiplexing (DataMUX), a technique that enables deep neural networks to process multiple inputs simultaneously using a single compact representation. DataMUX demonstrates that neural networks are capable of generating accurate predictions over mixtures of inputs, resulting in increased throughput with minimal extra memory requirements. Our approach uses two key components -- 1) a multiplexing layer that performs a fixed linear transformation to each input before combining them to create a mixed representation of the same size as a single input, which is then processed by the base network, and 2) a demultiplexing layer that converts the base network's output back into independent representations before producing predictions for each input. We show the viability of DataMUX for different architectures (Transformers, and to a lesser extent MLPs and CNNs) across six different tasks spanning sentence classification, named entity recognition and image classification. For instance, DataMUX for Transformers can multiplex up to 20x/40x inputs, achieving 11x/18x increase in throughput with minimal absolute performance drops of <2% and 4% respectively on MNLI, a natural language inference task. We also provide a theoretical construction for multiplexing in self-attention networks and analyze the effect of various design elements in DataMUX. Reading List: [ https://docs.google.com/document/d/1Pi-Pd7NwcScb1UfbHaGaE_g_-6-cwqXM_18yQ_u… | https://docs.google.com/document/d/1Pi-Pd7NwcScb1UfbHaGaE_g_-6-cwqXM_18yQ_u… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Samyak Gupta will present his General Exam "Attacking Federated Natural Language Modeling and Building Tools for Optimizing Cognitive Models" on Friday, April 15, 2022 at 1:00 PM in CS 302 and via zoom.
by Louis W. Riehl 11 Apr '22

11 Apr '22

Samyak Gupta will present his General Exam "Attacking Federated Natural Language Modeling and Building Tools for Optimizing Cognitive Models" on Friday, April 15, 2022 at 1:00 PM in CS 302 and via zoom. Zoom link: [ https://princeton.zoom.us/my/samyakg | https://princeton.zoom.us/my/samyakg ] Committee Members: Kai Li (advisor), Danqi Chen, Jon Cohen Abstract: In this talk, I will present two distinct areas of my work. In the first half of my talk, I describe an application related to privacy in federated learning. Federated learning allows distributed users to collaboratively train a model while keeping each user’s data private. As a result, it is actively being considered for privacy sensitive applications such as virtual mobile keyboards and analysis of electronic health records in hospitals. A growing body of recent work suggests that a clever eavesdropper may effectively reconstruct image data from gradients transmitted during federated learning. However, little progress has been made regarding the recovery of text data. Moreover, existing literature does not establish any metrics which may be used to quantify the severity of text data leakage. In this presentation, I present the first attack which can reconstruct sentences of private text data during training of a neural language model in federated learning. Our proposed method is built on beam search for text generation, by recovering a set of words from gradients and leveraging the latent memorization of text by large language models. We experiment with a GPT-2 model on several large datasets and demonstrate that our proposed attack can successfully recover private text data during training. Reading List: [ https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… | https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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UPDATE: Tianyu Gao will present his General Exam "SimCSE: Simple Contrastive Learning of Sentence Embeddings" on Wednesday, April 13, 2022 at 3:00 PM in CS 402 and via Zoom.
by Louis Riehl 11 Apr '22

11 Apr '22

Tianyu Gao will present his General Exam "SimCSE: Simple Contrastive Learning of Sentence Embeddings" on Wednesday, April 13, 2022 at 3:00 PM in CS 402 and via Zoom. Zoom link: https://princeton.zoom.us/j/95387462065 Committee Members: Danqi Chen (advisor), Karthik Narasimhan, Sanjeev Arora Abstract: We present SimCSE, a simple contrastive learning framework that greatly advances the state-of-the-art sentence embeddings. We first describe an unsupervised approach, which takes an input sentence and predicts itself in a contrastive objective, with only standard dropout used as noise. This simple method works surprisingly well, performing on par with previous supervised counterparts. We find that dropout acts as minimal data augmentation and removing it leads to a representation collapse. Then, we propose a supervised approach, which incorporates annotated pairs from natural language inference datasets into our contrastive learning framework, by using "entailment" pairs as positives and "contradiction" pairs as hard negatives. We evaluate SimCSE on standard semantic textual similarity (STS) tasks, and our unsupervised and supervised models using BERT base achieve an average of 76.3% and 81.6% Spearman's correlation respectively, a 4.2% and 2.2% improvement compared to previous best results. We also show -- both theoretically and empirically -- that contrastive learning objective regularizes pre-trained embeddings' anisotropic space to be more uniform, and it better aligns positive pairs when supervised signals are available. Reading List: https://docs.google.com/document/d/1fSGNfvOSkPggmMZUv_rAp4MK0fo05EeL9enjoyep 0Og/edit?usp=sharing Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Ilya Chugunov will present his General Exam "Implicit Inverse Imaging with Explicit Priors" on Monday, April 18, 2022 at 10 am in Friend Center 202 and via Zoom.
by Louis W. Riehl 11 Apr '22

11 Apr '22

Ilya Chugunov will present his General Exam "Implicit Inverse Imaging with Explicit Priors" on Monday, April 18, 2022 at 10 am in Friend Center 202 and via Zoom. Zoom link: [ https://princeton.zoom.us/j/91472663309 | https://princeton.zoom.us/j/91472663309 ] Committee Members: Felix Heide (advisor), Szymon Rusinkiewicz, Jia Deng Abstract: Armed with the right math, we can solve many tough imaging problems analytically, with no train time and no train data. Unfortunately, from lens aberrations to high level semantic features, the real visual world is full of things we either can’t or don’t want to model, but which we can learn from data. After all, we’ve yet to mathematically define a dog, but even a simple U-Net can be trained to recognize one. In this exam I present three works which try to combine analytic priors with a learned reconstruction pipeline: Mask-ToF, which uses differentiable simulation of time-of-flight imaging to help learn a physical augmentation to depth camera hardware in order to reduce imaging artifacts; GHz-ToF, which uses this same simulation to train a network for correction ambiguity in phase measurements; and my most recent work on handheld neural depth refinement, wherein we train an implicit model to recover fine depth of tabletop objects from a single smartphone camera snapshot. Reading List: [ https://docs.google.com/document/d/1tLs9TnnLzUGX6ebPPVpXsvLgsiCirsKl2Jxo_ji… | https://docs.google.com/document/d/1tLs9TnnLzUGX6ebPPVpXsvLgsiCirsKl2Jxo_ji… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.

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Thomas Macrina will present his Pre-FPO "Precise Alignment of Serial Section Electron Microscopy Images and Analysis of Neural Circuits" on Tuesday, April 12, 2022 at 5 pm EDT via Zoom.
by Louis W. Riehl 08 Apr '22

08 Apr '22

Thomas Macrina will present his Pre-FPO "Precise Alignment of Serial Section Electron Microscopy Images and Analysis of Neural Circuits" on Tuesday, April 12, 2022 at 5 pm EDT via Zoom. Zoom link: https://princeton.zoom.us/j/99057082150 Committee Members: H. Sebastian Seung(Examiner, advisor), Mala Murthy (Examiner), Felix Heide(examiner), Kai Li (reader), Stephan Saalfeld (HHMI Janelia, reader). All are welcome to attend. Title: Precise Alignment of Serial Section Electron Microscopy Images and Analysis of Neural Circuits Abstract: Neural circuits provide a means to test how the structure of a brain is related to its function, but reconstructing complete circuits in awhile mammalian brain is currently not possible. A promising path is to image a brain with serial section electron microscopy and use automatic methods tore construct circuits from the data. The imaging process introduces significant physical distortion that causes the automatic methods to make errors, so it's crucial to remove the distortion in a step called alignment. There are existing approaches to alignment, and by augmenting them with human-in-the-loop intervention we were able to produce a very accurate alignment of a five teravoxel mouse cortex dataset. This approach failed to correct distortion caused by cracks and folds, and the amount of human effort involved made it prohibitively expensive to process larger datasets. To overcome these limitations, our group developed new alignment pipeline that uses deep optic flow models to predict more precise distortion measurements. Using this new alignment pipeline, we have been able to accurately align much larger datasets, including a 100 teravoxel whole fly brain and a one petavoxel cubic millimeter of mouse cortex. These alignments enabled high quality automatic reconstructions that are now being proofread with manageable human effort to produce some of the largest neural circuits to date. With improvements in the reconstruction technology, like our new alignment pipeline, neural circuits are becoming more accessible, and there will be more effort put toward analyzing them. We analyzed a neural circuit reconstructed from the terascale mouse cortex dataset, finding that sizes of synapses in a shared layer 2/3 pyramidal neuron connection have a correlated binary component, and that the network of these pyramidal neurons is well-described by a configuration model. Both results may have implications for how to constrain biological models of memory or neural network organization. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Robotics Seminar: Cameron Aubin TODAY at 10:45am
by Emily C. Lawrence 07 Apr '22

07 Apr '22

ROBOTICS SEMINAR Speaker: Cameron Aubin Title: Towards enduring autonomous robots via embodied energy Day: Thursday, April 7, 2022 Time: 10:45-11:45am Location: Friend Center Convocation Room (FC113) Host: Glaucio Paulino Abstract: Modern robots lack the combination of endurance and adaptability that we see in living organisms. One explanation for this phenomenon is that robots are typically composed of individual power, actuation, sensory, and control blocks, each existing as separate materials and optimized for different tasks. Conversely, biological systems are highly interconnected, hierarchical, and capable of self-assembly, thereby enabling increased complexity and multifunctionality. Many robots also house energy in a single storage unit, often a battery, while more advanced organisms distribute energy throughout their entire bodies. In this talk, I will discuss my work developing robots with multifunctional embodied energy systems that increase their performance and endurance. My discussion will focus on the creation of an untethered soft robotic fish containing a “synthetic vascular system” inspired by redox flow batteries. This system combines the functions of mechanical actuation with hydraulic force transfer and electrolyte-based energy storage, increasing the energy density of the robot by over 300%. Next, I will discuss how energy-dense chemical fuels can be used to create powerful (Pstroke > 50W, Pspec = 147W/g) soft microactuators. We have leveraged microscale combustion and localized flame quenching to develop two demonstrations: a soft haptic display that operates at high frequencies (ƒ > 1kHz) without electromechanical valves and a quadrupedal microrobot capable of multigait/directional movement. Using energy-dense fuels at this scale can increase operating time, power density, and ultimately untether microrobotic systems. Bio: Cameron is a PhD candidate in the Department of Mechanical and Aerospace Engineering at Cornell University. He is a member of the Organic Robotics Laboratory and is advised by Dr. Robert Shepherd. Cameron’s work centers on improving the endurance, adaptability, and autonomy of robots by embodying them with multifunctional, biologically-inspired energy systems. His interests also include soft robotics, advanced manufacturing, and microrobotics. Cameron earned a B.S.E in Biomedical Engineering from Duke University in 2014 and an M.S. in Mechanical Engineering from Cornell University in 2019. His is a recipient of the Harriet Davis Fellowship and his research has been featured in several popular media outlets, including CNN, PBS, BBC, Wired, and more.

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