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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...
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09/07/2022
Prior Knowledge-Guided Attention in Self-Supervised Vision Transformers
Recent trends in self-supervised representation learning have focused on...
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08/16/2022
Context-Aware Streaming Perception in Dynamic Environments
Efficient vision works maximize accuracy under a latency budget. These w...
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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 ...
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04/28/2022
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
Machine learning has advanced dramatically, narrowing the accuracy gap t...
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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...
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02/17/2022
On Guiding Visual Attention with Language Specification
While real world challenges typically define visual categories with lang...
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08/25/2021
FogROS: An Adaptive Framework for Automating Fog Robotics Deployment
As many robot automation applications increasingly rely on multi-core pr...
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08/23/2021
C5T5: Controllable Generation of Organic Molecules with Transformers
Methods for designing organic materials with desired properties have hig...
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06/18/2021
MADE: Exploration via Maximizing Deviation from Explored Regions
In online reinforcement learning (RL), efficient exploration remains par...
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04/21/2021
Carbon Emissions and Large Neural Network Training
The computation demand for machine learning (ML) has grown rapidly recen...
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07/15/2020
FetchSGD: Communication-Efficient Federated Learning with Sketching
Existing approaches to federated learning suffer from a communication bo...
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01/08/2020
HyperSched: Dynamic Resource Reallocation for Model Development on a Deadline
Prior research in resource scheduling for machine learning training work...
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11/26/2019
Domain-Aware Dynamic Networks
Deep neural networks with more parameters and FLOPs have higher capacity...
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08/04/2019
Deep Reinforcement Learning in System Optimization
The recent advancements in deep reinforcement learning have opened new h...
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06/10/2019
ANODEV2: A Coupled Neural ODE Evolution Framework
It has been observed that residual networks can be viewed as the explici...
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12/31/2018
Dynamic Space-Time Scheduling for GPU Inference
Serving deep neural networks in latency critical interactive settings of...
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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...
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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...
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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...
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03/24/2018
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Large-scale labeled training datasets have enabled deep neural networks ...
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12/26/2017
Ray RLLib: A Composable and Scalable Reinforcement Learning Library
Reinforcement learning (RL) algorithms involve the deep nesting of disti...
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11/22/2017
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Neural networks rely on convolutions to aggregate spatial information. H...
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02/20/2017
