2021 Archive
Date | Host | Topic | Paper(s) Read | Participants | Notes |
May 8, 2021 | Tejas Gokhale | Uncertainty Sets for Image Classification |
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Sheng Cheng, Joshua Feinglass, Tejas Gokhale, Blake Harrison, Ishan Khurjekar, Yiran Luo | uncertainty, prediction sets vs single prediction, coverage |
May 15, 2021 | Blake Harrison | Imagination in Navigation |
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Sheng Cheng, Joshua Feinglass, Tejas Gokhale, Blake Harrison, Yiran Luo | wave function collapse, exploration, imagination, voxels and graphs |
May 22, 2021 | Yiran Luo | CLIP |
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May 29, 2021 | Tejas Gokhale | Implicit Neural Representations | first in-person meeting of the group. proof | ||
June 12, 2021 | Sheng Cheng | Part-Whole Hierarchies |
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June 23, 2021 | Joshua Feinglass | Evaluation Metrics I (Image Captioning) |
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June 30, 2021 | Blake Harrison | Evolutionary Strategies | |||
July 14, 2021 | Tejas Gokhale | Test-Time Training | |||
July 21, 2021 | Albert Reed | Neural Radiance Fields | |||
July 28, 2021 | David Ramirez | Evaluation Metrics II (Text Generation) |
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Aug 4, 2021 | Blake Harrison | RL for Game Playing |
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Aug 11, 2021 | Embodied Perception |
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Aug 18, 2021 | Video Feature Extraction |
2020 Archive
Date | Host | Topic | Paper(s) Read | Participants | Notes |
May 16, 2020 | Tejas Gokhale | Curriculum Learning | Tejas Gokhale, Joshua Feinglass, Kowshik Thopalli, Man Luo, Zhiyuan Fang | Connection with active learning, curriculum learning in GANs?, curriculum learning for domain adaptation | |
May 23, 2020 | Kowshik Thopalli / Tejas Gokhale | Active Learning | Kowshik Thopalli, Bhargav Ghanekar, Ishan Khurjekar, Joshua Feinglass, Man Luo, Sheng Cheng, Tejas Gokhale | Strategies for selecting samples to label, how to select the best samples that improve performance vs heuristic-based selection?, Schrodinger's Douchebags | |
May 30, 2020 | Kowshik Thopalli | Multi-modal Fusion |
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Kowshik Thopalli, Bhargav Ghanekar, Ishan Khurjekar, Joshua Feinglass, Man Luo, Sheng Cheng, Tejas Gokhale | Use-cases, when is it critical, audio-video alignment, modalities at different sampling rates, self-driving, |
June 6, 2020 | Ishan Khurjekar | Uncertainty Estimation I |
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Ishan Khurjekar, Bhargav Ghanekar, Joshua Feinglass, Kowshik Thopalli, Man Luo, Sheng Cheng, Tejas Gokhale | Uncertainty, epistemic vs sensing, uncerrtainty estimation --> stronger metrics for evaluation of models, |
June 13, 2020 | Joshua Feinglass | Transformers, BERT, VilBERT/LXMERT | Joshua Feinglass, Bhargav Ghanekar, Ishan Khurjekar, Kowshik Thopalli, Man Luo, Sheng Cheng, Tejas Gokhale | Attention, Self-Attention, Encoder-Decoder Architecture, Transformer Decoder, BERT pre-training tasks and intuition behind their choice, extension to cross-modal (vision+language) pre-training | |
June 20, 2020 | Bhargav Ghanekar | Computational Imaging | Bhargav Ghanekar, Joshua Feinglass, Ishan Khurjekar, Kowshik Thopalli, Man Luo, Sheng Cheng, Tejas Gokhale | Point Spread Function, Relation between Defocus Blur and Depth, Designing Phase Masks through end-to-end learning | |
June 27, 2020 | Ishan Khurjekar | Uncertainty Estimation II |
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Ishan Khurjekar, Bhargav Ghanekar, Changhoon Kim, Man Luo, Sheng Cheng, Tejas Gokhale | Combining Aleatoric and Epistemic Uncertainty. Heteroscedastic Uncertainty as Learned Loss Attenuation: (1) inputs with high predicted uncertainty will have a smaller effect on the loss, (2) model is discouraged from predicting very low uncertainty for points with high residual error, |
July 4, 2020 | Changhoon Kim | Adversarial Attack |
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Changhoon Kim, Ishan Khurjekar, Joshua Feinglass, Kowshik Thopalli, Man Luo, Sheng Cheng, Tejas Gokhale | Adversarial Attacks against malicious generative neural networks (DeepFake / DeepNude), extensions, generalization to unseen networks, ensembles |
July 11, 2020 | Sheng Cheng | Contrastive Learning | Sheng Cheng, Bhargav Ghanekar, Ishan Khurjekar, Joshua Feinglass, Man Luo, Tejas Gokhale | Contrastive Learning as a self-supervised learning framework, connection with hashing, auto-encoders, language pretraining, word2vec. Open discussion on many faces of generalization. | |
July 18, 2020 | Man Luo | Neuro-Symbolic Learning | Man Luo, Sheng Cheng, Bhargav Ghanekar, Ishan Khurjekar, Joshua Feinglass, Tejas Gokhale | symbolic logic, super-quick intro to knowledge representation (propositional, FOL), demo of a reasoning problem in CLINGO, logic tensor networks, open-ended discussion about application in vision-and-language. | |
July 25, 2020 | Tejas Gokhale | Reinforcement Learning |
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Tejas Gokhale, Sheng Cheng, Bhargav Ghanekar, Ishan Khurjekar, Joshua Feinglass, Man Luo | RL vocabulary: state, action, reward, policy, discount factor. intuition behind experential replay and discounted reward, simpler example: navigation from (0, 0) to (10, 10). SARSA. Breakthrough in learning to play Atari games. |
August 1, 2020 | Joshua Feinglass | Information Theory |
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Joshua Feinglass, Sheng Cheng, Ishan Khurjekar, Man Luo, Tejas Gokhale | Definitions of information, mututal information, entropy, Fischer Information, random thoughts about Huffmann coding and Wavelet compression, InfoGAN, conditional GAN, pitfalls and tricks-of-the-trade for GAN training, mode collapse |
August 8, 2020 | - | cancelled | |||
August 15, 2020 | Man Luo | Information Retrieval | Man Luo, Sheng Cheng, Joshua Feinglass, Tejas Gokhale, Ishan Khurjekar, Yiran Luo | ||
August 22, 2020 | Tejas Gokhale | Convex Optimization | Tejas Gokhale, Sheng Cheng, Joshua Feinglass, Yiran Luo, Kuntal Pal. | Definitions of convex sets, functions. The general optimization problem. The convex optimization problem. Why is convexity nice? Lgrangian duality, steepest descent, gradient descent, proximal methods (projected GD), Newton. Input convex NN (convex inference) |
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August 29, 2020 | Ishan Khurjekar | Graph Neural Networks | Ishan Khurjekar, Man Luo, Yiran Luo, Sheng Cheng, Joshua Feinglass, Tejas Gokhale, Amrita Bhattacharjee, Weidong Zhang | Graph neural networks, aggregation functions, training (supervises/unsupervised), some applications, aggregation as convolution. | |
September 5, 2020 | - | cancelled | |||
September 12, 2020 | Tejas Gokhale | Causal Inference | TG,BG,IK,JF,YL,SC,ML | Causality notations: variables, interventions/treatments, outcomes, confounders, CI as a missing data problem, Learning causal models with NN, results on synthetic data, missing pieces/restrictive assumptions for real-world data |
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September 19, 2020 | Joshua Feinglass (transfer hosting)/ Kowshik Thopalli (supervision) / Tejas Gokhale (meta-hosting) | Meta Learning | JF,KT,TG,BG,IK,YL,SC,ML,AB | Learning, learning to learn, meta-train, meta-test, MAML algo discussion, use-cases, extensions with "unequal", "hierarchical", "unrelated" tasks, insights from KT about faster algos, taskonomy/task2vec ... | |
September 26, 2020 | Yiran Luo | Low Resource Machine Translation | |||
October 03, 2020 | Sheng Cheng | Super-resolution |