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09/17/2023
Analog Content-Addressable Memory from Complementary FeFETs
To address the increasing computational demands of artificial intelligen...
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08/06/2023
Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience
Machine learning (ML) has shown great promise for revolutionizing a numb...
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07/12/2023
Budgeting Counterfactual for Offline RL
The main challenge of offline reinforcement learning, where data is limi...
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05/29/2023
Taming AI Bots: Controllability of Neural States in Large Language Models
We tackle the question of whether an agent can, by suitable choice of pr...
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05/27/2023
Learning Capacity: A Measure of the Effective Dimensionality of a Model
We exploit a formal correspondence between thermodynamics and inference,...
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05/02/2023
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold
We develop information-geometric techniques to analyze the trajectories ...
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04/22/2023
Fast Diffusion Probabilistic Model Sampling through the lens of Backward Error Analysis
Denoising diffusion probabilistic models (DDPMs) are a class of powerful...
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03/15/2023
Mesh Strikes Back: Fast and Efficient Human Reconstruction from RGB videos
Human reconstruction and synthesis from monocular RGB videos is a challe...
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10/31/2022
A picture of the space of typical learnable tasks
We develop a technique to analyze representations learned by deep networ...
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10/04/2022
Data drift correction via time-varying importance weight estimator
Real-world deployment of machine learning models is challenging when dat...
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08/23/2022
The Value of Out-of-Distribution Data
More data helps us generalize to a task. But real datasets can contain o...
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08/02/2022
A Model for Perimeter-Defense Problems with Heterogeneous Teams
We develop a model of the multi-agent perimeter-defense game to calculat...
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06/11/2022
MammoDL: Mammographic Breast Density Estimation using Federated Learning
Assessing breast cancer risk from imaging remains a subjective process, ...
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05/26/2022
Machine Learning Models Are Not Necessarily Biased When Constructed Properly: Evidence from Neuroimaging Studies
Despite the great promise that machine learning has offered in many fiel...
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02/25/2022
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity
Interpretable machine learning has demonstrated impressive performance w...
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02/01/2022
Deep Reference Priors: What is the best way to pretrain a model?
What is the best way to exploit extra data – be it unlabeled data from t...
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01/19/2022
Prospective Learning: Back to the Future
Research on both natural intelligence (NI) and artificial intelligence (...
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06/12/2021
Harmonization with Flow-based Causal Inference
Heterogeneity in medical data, e.g., from data collected at different si...
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06/06/2021
Boosting a Model Zoo for Multi-Task and Continual Learning
Leveraging data from multiple tasks, either all at once, or incrementall...
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03/26/2021
Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
We propose a framework for deformable linear object prediction. Predicti...
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03/23/2021
Embracing the Disharmony in Heterogeneous Medical Data
Heterogeneity in medical imaging data is often tackled, in the context o...
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02/18/2021
Continuous Doubly Constrained Batch Reinforcement Learning
Reliant on too many experiments to learn good actions, current Reinforce...
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11/16/2020
Scalable Reinforcement Learning Policies for Multi-Agent Control
This paper develops a stochastic Multi-Agent Reinforcement Learning (MAR...
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11/01/2020
An Information-Geometric Distance on the Space of Tasks
This paper computes a distance between tasks modeled as joint distributi...
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08/17/2020
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation
Autonomous navigation in crowded, complex urban environments requires in...
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08/03/2020
Proximal Deterministic Policy Gradient
This paper introduces two simple techniques to improve off-policy Reinfo...
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06/26/2020
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning
This paper prescribes a suite of techniques for off-policy Reinforcement...
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06/25/2020
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation
Automated machine learning (AutoML) can produce complex model ensembles ...
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05/10/2020
BayesRace: Learning to race autonomously using prior experience
Learning to race autonomously is a challenging problem. It requires perc...
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04/06/2020
TraDE: Transformers for Density Estimation
We present TraDE, an attention-based architecture for auto-regressive de...
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02/27/2020
A Free-Energy Principle for Representation Learning
This paper employs a formal connection of machine learning with thermody...
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10/08/2019
Directional Adversarial Training for Cost Sensitive Deep Learning Classification Applications
In many real-world applications of Machine Learning it is of paramount i...
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09/30/2019
Meta-Q-Learning
This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm ...
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09/06/2019
A Baseline for Few-Shot Image Classification
Fine-tuning a deep network trained with the standard cross-entropy loss ...
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05/05/2019
P3O: Policy-on Policy-off Policy Optimization
On-policy reinforcement learning (RL) algorithms have high sample comple...
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10/30/2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks
Stochastic gradient descent (SGD) is widely believed to perform implicit...
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07/03/2017
Parle: parallelizing stochastic gradient descent
We propose a new algorithm called Parle for parallel training of deep ne...
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11/06/2016