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∙ 12/15/2022

Kernel-as-a-Service: A Serverless Interface to GPUs

Serverless computing has made it easier than ever to deploy applications...

0 Nathan Pemberton, et al. ∙

∙ 09/07/2022

Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers

Recent trends in self-supervised representation learning have focused on...

0 Kevin Miao, et al. ∙

∙ 08/16/2022

Context-Aware Streaming Perception in Dynamic Environments

Efficient vision works maximize accuracy under a latency budget. These w...

7 Gur-Eyal Sela, et al. ∙

∙ 05/19/2022

FogROS 2: An Adaptive and Extensible Platform for Cloud and Fog Robotics Using ROS 2

Mobility, power, and price points often dictate that robots do not have ...

0 Jeffrey Ichnowski, et al. ∙

∙ 04/28/2022

Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly

Machine learning has advanced dramatically, narrowing the accuracy gap t...

0 Spencer Whitehead, et al. ∙

∙ 04/11/2022

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Machine Learning (ML) workloads have rapidly grown in importance, but ra...

6 David Patterson, et al. ∙

∙ 02/17/2022

On Guiding Visual Attention with Language Specification

While real world challenges typically define visual categories with lang...

3 Suzanne Petryk, et al. ∙

∙ 08/25/2021

FogROS: An Adaptive Framework for Automating Fog Robotics Deployment

As many robot automation applications increasingly rely on multi-core pr...

0 Kaiyuan, et al. ∙

∙ 08/23/2021

C5T5: Controllable Generation of Organic Molecules with Transformers

Methods for designing organic materials with desired properties have hig...

0 Daniel Rothchild, et al. ∙

∙ 06/18/2021

MADE: Exploration via Maximizing Deviation from Explored Regions

In online reinforcement learning (RL), efficient exploration remains par...

4 Tianjun Zhang, et al. ∙

∙ 04/21/2021

Carbon Emissions and Large Neural Network Training

The computation demand for machine learning (ML) has grown rapidly recen...

17 David Patterson, et al. ∙

∙ 07/15/2020

FetchSGD: Communication-Efficient Federated Learning with Sketching

Existing approaches to federated learning suffer from a communication bo...

0 Daniel Rothchild, et al. ∙

∙ 01/08/2020

HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline

Prior research in resource scheduling for machine learning training work...

0 Richard Liaw, et al. ∙

∙ 11/26/2019

Domain-Aware Dynamic Networks

Deep neural networks with more parameters and FLOPs have higher capacity...

1 Tianyuan Zhang, et al. ∙

∙ 08/04/2019

Deep Reinforcement Learning in System Optimization

The recent advancements in deep reinforcement learning have opened new h...

6 Ameer Haj-Ali, et al. ∙

∙ 06/10/2019

ANODEV2: A Coupled Neural ODE Evolution Framework

It has been observed that residual networks can be viewed as the explici...

3 Tianjun Zhang, et al. ∙

∙ 12/31/2018

Dynamic Space-Time Scheduling for GPU Inference

Serving deep neural networks in latency critical interactive settings of...

1 Paras Jain, et al. ∙

∙ 11/30/2018

On the Computational Inefficiency of Large Batch Sizes for Stochastic Gradient Descent

Increasing the mini-batch size for stochastic gradient descent offers si...

0 Noah Golmant, et al. ∙

∙ 11/03/2018

ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale

The deployment of large camera networks for video analytics is an establ...

0 Samvit Jain, et al. ∙

∙ 07/17/2018

Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video

In this paper, we present Accel, a novel semantic video segmentation sys...

0 Samvit Jain, et al. ∙

∙ 03/24/2018

Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld

Large-scale labeled training datasets have enabled deep neural networks ...

0 Sicheng Zhao, et al. ∙

∙ 12/26/2017

Ray RLLib: A Composable and Scalable Reinforcement Learning Library

Reinforcement learning (RL) algorithms involve the deep nesting of disti...

1 Eric Liang, et al. ∙

∙ 11/22/2017

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

Neural networks rely on convolutions to aggregate spatial information. H...

0 Bichen Wu, et al. ∙

∙ 02/20/2017

Hemingway: Modeling Distributed Optimization Algorithms

Distributed optimization algorithms are widely used in many industrial m...

0 Xinghao Pan, et al. ∙

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