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08/21/2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Privacy-preserving machine learning aims to train models on private data...
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07/05/2023
Evaluating AI systems under uncertain ground truth: a case study in dermatology
For safety, AI systems in health undergo thorough evaluations before dep...
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02/08/2022
Competition-Level Code Generation with AlphaCode
Programming is a powerful and ubiquitous problem-solving tool. Developin...
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09/15/2021
Challenges in Detoxifying Language Models
Large language models (LM) generate remarkably fluent text and can be ef...
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06/26/2021
Inferring a Continuous Distribution of Atom Coordinates from Cryo-EM Images using VAEs
Cryo-electron microscopy (cryo-EM) has revolutionized experimental prote...
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04/14/2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
We improve the scalability of Branch and Bound (BaB) algorithms for form...
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12/23/2020
Solving Mixed Integer Programs Using Neural Networks
Mixed Integer Programming (MIP) solvers rely on an array of sophisticate...
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12/07/2020
Autoencoding Variational Autoencoder
Does a Variational AutoEncoder (VAE) consistently encode typical samples...
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11/14/2020
Towards transformation-resilient provenance detection of digital media
Advancements in deep generative models have made it possible to synthesi...
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10/28/2020
Training Generative Adversarial Networks by Solving Ordinary Differential Equations
The instability of Generative Adversarial Network (GAN) training has fre...
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10/22/2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Convex relaxations have emerged as a promising approach for verifying de...
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10/07/2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Adversarial training and its variants have become de facto standards for...
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07/10/2020
Contrastive Training for Improved Out-of-Distribution Detection
Reliable detection of out-of-distribution (OOD) inputs is increasingly u...
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07/09/2020
Evaluating the Apperception Engine
The Apperception Engine is an unsupervised learning system. Given a sequ...
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07/07/2020
Strong Generalization and Efficiency in Neural Programs
We study the problem of learning efficient algorithms that strongly gene...
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02/24/2020
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
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12/06/2019
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Recent research has made the surprising finding that state-of-the-art de...
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11/08/2019
Reducing Sentiment Bias in Language Models via Counterfactual Evaluation
Recent improvements in large-scale language models have driven progress ...
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10/28/2019
Learning Transferable Graph Exploration
This paper considers the problem of efficient exploration of unseen envi...
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10/21/2019
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Adversarial testing methods based on Projected Gradient Descent (PGD) ar...
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10/05/2019
Making sense of sensory input
This paper attempts to answer a central question in unsupervised learnin...
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10/03/2019
CLEVRER: CoLlision Events for Video REpresentation and Reasoning
The ability to reason about temporal and causal events from videos lies ...
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09/14/2019
Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critic...
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09/03/2019
Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation
Neural networks are part of many contemporary NLP systems, yet their emp...
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07/04/2019
Adversarial Robustness through Local Linearization
Adversarial training is an effective methodology for training deep neura...
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05/31/2019
Are Labels Required for Improving Adversarial Robustness?
Recent work has uncovered the interesting (and somewhat surprising) find...
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05/30/2019
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
Medical imaging only indirectly measures the molecular identity of the t...
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05/07/2019
REGAL: Transfer Learning For Fast Optimization of Computation Graphs
We present a deep reinforcement learning approach to optimizing the exec...
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04/29/2019
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This paper addresses the challenging problem of retrieval and matching o...
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04/26/2019
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
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04/26/2019
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that lear...
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04/05/2019
Structured agents for physical construction
Physical construction -- the ability to compose objects, subject to phys...
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04/02/2019
Analysing Mathematical Reasoning Abilities of Neural Models
Mathematical reasoning---a core ability within human intelligence---pres...
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03/28/2019
Meta-Learning surrogate models for sequential decision making
Meta-learning methods leverage past experience to learn data-driven indu...
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02/27/2019
Degenerate Feedback Loops in Recommender Systems
Machine learning is used extensively in recommender systems deployed in ...
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02/25/2019
Verification of Non-Linear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
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12/14/2018
Scaling shared model governance via model splitting
Currently the only techniques for sharing governance of a deep learning ...
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12/06/2018
Verification of deep probabilistic models
Probabilistic models are a critical part of the modern deep learning too...
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12/04/2018
Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures
This paper addresses the problem of evaluating learning systems in safet...
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12/04/2018
Compositional Imitation Learning: Explaining and executing one task at a time
We introduce a framework for Compositional Imitation Learning and Execut...
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11/22/2018
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
While deep learning has led to remarkable results on a number of challen...
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10/30/2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent works have shown that it is possible to train models that are ver...
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10/04/2018
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
We marry two powerful ideas: deep representation learning for visual rec...
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06/05/2018
Learning to Follow Language Instructions with Adversarial Reward Induction
Recent work has shown that deep reinforcement-learning agents can learn ...
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06/04/2018
Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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05/28/2018
Value Propagation Networks
We present Value Propagation (VProp), a parameter-efficient differentiab...
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05/25/2018
Training verified learners with learned verifiers
This paper proposes a new algorithmic framework,predictor-verifier train...
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05/23/2018
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
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05/11/2018
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Program synthesis is the task of automatically generating a program cons...
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04/06/2018