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06/07/2023
Proximity-Informed Calibration for Deep Neural Networks
Confidence calibration is central to providing accurate and interpretabl...
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04/21/2023
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
It is widely acknowledged that large models have the potential to delive...
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02/06/2023
Trust, but Verify: Using Self-Supervised Probing to Improve Trustworthiness
Trustworthy machine learning is of primary importance to the practical d...
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11/29/2022
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
How do we know when the predictions made by a classifier can be trusted?...
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11/10/2022
Coordinating CAV Swarms at Intersections with a Deep Learning Model
Connected and automated vehicles (CAVs) are viewed as a special kind of ...
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10/19/2022
lo-fi: distributed fine-tuning without communication
When fine-tuning large neural networks, it is common to use multiple nod...
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08/23/2022
Neural PCA for Flow-Based Representation Learning
Of particular interest is to discover useful representations solely from...
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05/23/2022
Parameter-Efficient Sparsity for Large Language Models Fine-Tuning
With the dramatically increased number of parameters in language models,...
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12/06/2021
A General Framework for Debiasing in CTR Prediction
Most of the existing methods for debaising in click-through rate (CTR) p...
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12/02/2021
Probabilistic Contrastive Loss for Self-Supervised Learning
This paper proposes a probabilistic contrastive loss function for self-s...
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10/31/2021
Enhancing Top-N Item Recommendations by Peer Collaboration
Deep neural networks (DNN) have achieved great success in the recommende...
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10/18/2021
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Consistent state estimation is challenging, especially under the epistem...
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06/04/2021
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature Gradient
Despite superior performance on various natural language processing task...
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05/31/2021
1×N Block Pattern for Network Sparsity
Though network sparsity emerges as a promising direction to overcome the...
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04/21/2021
Tackling Variabilities in Autonomous Driving
The state-of-the-art driving automation system demands extreme computati...
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04/18/2021
Continuity-Discrimination Convolutional Neural Network for Visual Object Tracking
This paper proposes a novel model, named Continuity-Discrimination Convo...
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03/26/2021
Reactive Task and Motion Planning under Temporal Logic Specifications
We present a task-and-motion planning (TAMP) algorithm robust against a ...
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02/05/2021
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers
The size of Transformer models is growing at an unprecedented pace. It h...
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06/28/2020
PyTorch Distributed: Experiences on Accelerating Data Parallel Training
This paper presents the design, implementation, and evaluation of the Py...
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04/06/2020
Quantum Inspired Word Representation and Computation
Word meaning has different aspects, while the existing word representati...
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09/27/2019
Identifying through Flows for Recovering Latent Representations
Identifiability, or recovery of the true latent representations from whi...
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08/06/2019
Self-Balanced Dropout
Dropout is known as an effective way to reduce overfitting via preventin...
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06/07/2019
Planning With Uncertain Specifications (PUnS)
Reward engineering is crucial to high performance in reinforcement learn...
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08/30/2018
Generalize Symbolic Knowledge With Neural Rule Engine
Neural-symbolic learning aims to take the advantages of both neural netw...
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08/24/2018
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
Dropout is used to avoid overfitting by randomly dropping units from the...
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05/12/2018
Analogical Reasoning on Chinese Morphological and Semantic Relations
Analogical reasoning is effective in capturing linguistic regularities. ...
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04/20/2018