1k citations · 8k across the 112 of their papers we have counts for
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SensorLM: Learning the Language of Wearable Sensors
Yuwei Zhang, Kumar Ayush, Siyuan Qiao +17
We present SensorLM, a family of sensor-language foundation models that enable wearable sensor data understanding with natural language. Despite its pervasive nature, aligning and…
LSM-2: Learning from Incomplete Wearable Sensor Data
Maxwell A. Xu, Girish Narayanswamy, Kumar Ayush +22
Foundation models, a cornerstone of recent advancements in machine learning, have predominantly thrived on complete and well-structured data. Wearable sensor data frequently suffer…
Scaling Wearable Foundation Models
Girish Narayanswamy, Xin Liu, Kumar Ayush +15
Wearable sensors have become ubiquitous thanks to a variety of health tracking features. The resulting continuous and longitudinal measurements from everyday life generate large vo…
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Leonard Berrada, Soham De, Judy Hanwen Shen +6
Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework…
Evaluating AI systems under uncertain ground truth: a case study in dermatology
David Stutz, Ali Taylan Cemgil, Abhijit Guha Roy +17
For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However…
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…
Autoencoding Variational Autoencoder
A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham +2
Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a…
Towards transformation-resilient provenance detection of digital media
Jamie Hayes, Krishnamurthy, Dvijotham +4
Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin +8
Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Progr…
Contrastive Training for Improved Out-of-Distribution Detection
Jim Winkens, Rudy Bunel, Abhijit Guha Roy +10
Reliable detection of out-of-distribution (OOD) inputs is increasingly understood to be a precondition for deployment of machine learning systems. This paper proposes and investiga…
Strong Generalization and Efficiency in Neural Programs
Yujia Li, Felix Gimeno, Pushmeet Kohli +1
We study the problem of learning efficient algorithms that strongly generalize in the framework of neural program induction. By carefully designing the input / output interfaces of…
Lagrangian Decomposition for Neural Network Verification
Rudy Bunel, Alessandro De Palma, Alban Desmaison +4
A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, d…
Achieving Robustness in the Wild via Adversarial Mixing with Disentangled Representations
Sven Gowal, Chongli Qin, Po-Sen Huang +4
Recent research has made the surprising finding that state-of-the-art deep learning models sometimes fail to generalize to small variations of the input. Adversarial training has b…
Learning Transferable Graph Exploration
Hanjun Dai, Yujia Li, Chenglong Wang +3
This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a…
An Alternative Surrogate Loss for PGD-based Adversarial Testing
Sven Gowal, Jonathan Uesato, Chongli Qin +3
Adversarial testing methods based on Projected Gradient Descent (PGD) are widely used for searching norm-bounded perturbations that cause the inputs of neural networks to be miscla…
Branch and Bound for Piecewise Linear Neural Network Verification
Rudy Bunel, Jingyue Lu, Ilker Turkaslan +3
The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context…
Structured agents for physical construction
Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch +4
Physical construction---the ability to compose objects, subject to physical dynamics, to serve some function---is fundamental to human intelligence. We introduce a suite of challen…
Graph Matching Networks for Learning the Similarity of Graph Structured Objects
Yujia Li, Chenjie Gu, Thomas Dullien +2
This paper addresses the challenging problem of retrieval and matching of graph structured objects, and makes two key contributions. First, we demonstrate how Graph Neural Networks…
Are Labels Required for Improving Adversarial Robustness?
Jonathan Uesato, Jean-Baptiste Alayrac, Po-Sen Huang +3
Recent work has uncovered the interesting (and somewhat surprising) finding that training models to be invariant to adversarial perturbations requires substantially larger datasets…
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs
Aditya Paliwal, Felix Gimeno, Vinod Nair +4
We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based wor…
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham +3
Models such as Sequence-to-Sequence and Image-to-Sequence are widely used in real world applications. While the ability of these neural architectures to produce variable-length out…
Analysing Mathematical Reasoning Abilities of Neural Models
David Saxton, Edward Grefenstette, Felix Hill +1
Mathematical reasoning---a core ability within human intelligence---presents some unique challenges as a domain: we do not come to understand and solve mathematical problems primar…
Verification of Non-Linear Specifications for Neural Networks
Chongli Qin, Krishnamurthy, Dvijotham +7
Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adve…
Scaling shared model governance via model splitting
Miljan Martic, Jan Leike, Andrew Trask +3
Currently the only techniques for sharing governance of a deep learning model are homomorphic encryption and secure multiparty computation. Unfortunately, neither of these techniqu…