105 citations · 267 across the 9 of their papers we have counts for
9 papers
Online semi-supervised perception: Real-time learning without explicit feedback
Branislav Kveton, Michal Valko, Matthai Phillipose +1
This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graphs and online learning. In pa…
Neurosymbolic Language Reasoning as Satisfiability Modulo Theory
Hyunseok Oh, Sam Stern, Youngki Lee +1
Natural language understanding requires interleaving textual and logical reasoning, yet large language models often fail to perform such reasoning reliably. Existing neurosymbolic…
Symphony: Optimized DNN Model Serving using Deferred Batch Scheduling
Lequn Chen, Weixin Deng, Anirudh Canumalla +4
Having large batch sizes is one of the most critical aspects of increasing the accelerator efficiency and the performance of DNN model inference. However, existing model serving sy…
Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
Josh Fromm, Shwetak Patel, Matthai Philipose
Recent work has shown that fast, compact low-bitwidth neural networks can be surprisingly accurate. These networks use homogeneous binarization: all parameters in each layer or (mo…
Focus: Querying Large Video Datasets with Low Latency and Low Cost
Kevin Hsieh, Ganesh Ananthanarayanan, Peter Bodik +4
Large volumes of videos are continuously recorded from cameras deployed for traffic control and surveillance with the goal of answering "after the fact" queries: identify video fra…
Precision Scaling of Neural Networks for Efficient Audio Processing
Jong Hwan Ko, Josh Fromm, Matthai Philipose +2
While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In t…
Fast Video Classification via Adaptive Cascading of Deep Models
Haichen Shen, Seungyeop Han, Matthai Philipose +1
Recent advances have enabled "oracle" classifiers that can classify across many classes and input distributions with high accuracy without retraining. However, these classifiers ar…
Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
Gregor Urban, Krzysztof J. Geras, Samira Ebrahimi Kahou +6
Yes, they do. This paper provides the first empirical demonstration that deep convolutional models really need to be both deep and convolutional, even when trained with methods suc…
Blending LSTMs into CNNs
Krzysztof J. Geras, Abdel-rahman Mohamed, Rich Caruana +6
We consider whether deep convolutional networks (CNNs) can represent decision functions with similar accuracy as recurrent networks such as LSTMs. First, we show that a deep CNN wi…