Search Results for author: Brian Cheung

Found 17 papers, 7 papers with code

Meta-Learning and Self-Supervised Pretraining for Real World Image Translation

no code implementations22 Dec 2021 Ileana Rugina, Rumen Dangovski, Mark Veillette, Pooya Khorrami, Brian Cheung, Olga Simek, Marin Soljačić

In recent years, emerging fields such as meta-learning or self-supervised learning have been closing the gap between proof-of-concept results and real-life applications of machine learning by extending deep-learning to the semi-supervised and few-shot domains.

Image-to-Image Translation Meta-Learning +2

Equivariant Contrastive Learning

2 code implementations28 Oct 2021 Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljačić

In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.

Contrastive Learning Self-Supervised Learning

Equivariant Self-Supervised Learning: Encouraging Equivariance in Representations

no code implementations ICLR 2022 Rumen Dangovski, Li Jing, Charlotte Loh, Seungwook Han, Akash Srivastava, Brian Cheung, Pulkit Agrawal, Marin Soljacic

In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations prescribed from human knowledge.

Self-Supervised Learning

Recurrent Parameter Generators

no code implementations15 Jul 2021 Jiayun Wang, Yubei Chen, Stella X. Yu, Brian Cheung, Yann Lecun

Specifically, for a network, we create a recurrent parameter generator (RPG), from which the parameters of each convolution layer are generated.

Model Compression

The Low-Rank Simplicity Bias in Deep Networks

1 code implementation18 Mar 2021 Minyoung Huh, Hossein Mobahi, Richard Zhang, Brian Cheung, Pulkit Agrawal, Phillip Isola

We show empirically that our claim holds true on finite width linear and non-linear models on practical learning paradigms and show that on natural data, these are often the solutions that generalize well.

Image Classification

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings

1 code implementation ICML 2020 Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman

Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous, imperiling the RL agent, other agents, and the environment.

reinforcement-learning

Word Embedding Visualization Via Dictionary Learning

no code implementations9 Oct 2019 Juexiao Zhang, Yubei Chen, Brian Cheung, Bruno A. Olshausen

Co-occurrence statistics based word embedding techniques have proved to be very useful in extracting the semantic and syntactic representation of words as low dimensional continuous vectors.

Dictionary Learning

Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents

no code implementations25 Sep 2019 Jesse Zhang, Brian Cheung, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure?

Domain Adaptation Meta Reinforcement Learning +1

Learning Unsupervised Learning Rules

no code implementations ICLR 2019 Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Here, our desired task (meta-objective) is the performance of the representation on semi-supervised classification, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations that perform well under this meta-objective.

Meta-Learning

Superposition of many models into one

1 code implementation NeurIPS 2019 Brian Cheung, Alex Terekhov, Yubei Chen, Pulkit Agrawal, Bruno Olshausen

We present a method for storing multiple models within a single set of parameters.

Meta-Learning Update Rules for Unsupervised Representation Learning

2 code implementations ICLR 2019 Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein

Specifically, we target semi-supervised classification performance, and we meta-learn an algorithm -- an unsupervised weight update rule -- that produces representations useful for this task.

Meta-Learning Representation Learning

Generalization Challenges for Neural Architectures in Audio Source Separation

1 code implementation23 Mar 2018 Shariq Mobin, Brian Cheung, Bruno Olshausen

Recent work has shown that recurrent neural networks can be trained to separate individual speakers in a sound mixture with high fidelity.

Audio Source Separation

Adversarial Examples that Fool both Computer Vision and Time-Limited Humans

no code implementations NeurIPS 2018 Gamaleldin F. Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alex Kurakin, Ian Goodfellow, Jascha Sohl-Dickstein

Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich.

Discovering Hidden Factors of Variation in DeepNetworks

no code implementations arXiv 2015 Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen

Deep learning has enjoyed a great deal of success because of its ability to learnuseful features for tasks such as classification.

General Classification

Discovering Hidden Factors of Variation in Deep Networks

1 code implementation20 Dec 2014 Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen

Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification.

General Classification

Fast Simultaneous Training of Generalized Linear Models (FaSTGLZ)

no code implementations31 Jul 2013 Bryan R. Conroy, Jennifer M. Walz, Brian Cheung, Paul Sajda

We present an efficient algorithm for simultaneously training sparse generalized linear models across many related problems, which may arise from bootstrapping, cross-validation and nonparametric permutation testing.

General Classification

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