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05/24/2023
Flan-MoE: Scaling Instruction-Finetuned Language Models with Sparse Mixture of Experts
The explosive growth of language models and their applications have led ...
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04/03/2023
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
The goal of open-vocabulary detection is to identify novel objects based...
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03/13/2023
Scaling Vision-Language Models with Sparse Mixture of Experts
The field of natural language processing (NLP) has made significant stri...
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02/15/2023
Big Little Transformer Decoder
The recent emergence of Large Language Models based on the Transformer a...
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02/08/2023
Q-Diffusion: Quantizing Diffusion Models
Diffusion models have achieved great success in synthesizing diverse and...
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06/22/2022
The ArtBench Dataset: Benchmarking Generative Models with Artworks
We introduce ArtBench-10, the first class-balanced, high-quality, cleanl...
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06/02/2022
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
The recently proposed Conformer model has become the de facto backbone m...
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07/13/2021
How Much Can CLIP Benefit Vision-and-Language Tasks?
Most existing Vision-and-Language (V L) models rely on pre-trained vis...
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07/02/2021
Learned Token Pruning for Transformers
A major challenge in deploying transformer models is their prohibitive i...
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05/30/2021
MLPruning: A Multilevel Structured Pruning Framework for Transformer-based Models
Pruning is an effective method to reduce the memory footprint and comput...
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03/31/2021
Q-ASR: Integer-only Zero-shot Quantization for Efficient Speech Recognition
End-to-end neural network models achieve improved performance on various...
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01/22/2021
Hessian-Aware Pruning and Optimal Neural Implant
Pruning is an effective method to reduce the memory footprint and FLOPs ...
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01/05/2021
I-BERT: Integer-only BERT Quantization
Transformer based models, like BERT and RoBERTa, have achieved state-of-...
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11/30/2020
Annotation-Efficient Untrimmed Video Action Recognition
Deep learning has achieved great success in recognizing video actions, b...
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11/20/2020
HAWQV3: Dyadic Neural Network Quantization
Quantization is one of the key techniques used to make Neural Networks (...
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09/16/2020
Evaluating Self-Supervised Pretraining Without Using Labels
A common practice in unsupervised representation learning is to use labe...
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06/23/2020
Rethinking Distributional Matching Based Domain Adaptation
Domain adaptation (DA) is a technique that transfers predictive models t...
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06/12/2020
CoDeNet: Algorithm-hardware Co-design for Deformable Convolution
Deploying deep learning models on embedded systems for computer vision t...
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06/05/2020
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
Computer vision has achieved great success using standardized image repr...
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02/26/2020
Multi-source Domain Adaptation in the Deep Learning Era: A Systematic Survey
In many practical applications, it is often difficult and expensive to o...
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12/16/2019
PyHessian: Neural Networks Through the Lens of the Hessian
We present PyHessian, a new scalable framework that enables fast computa...
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10/07/2019
Checkmate: Breaking the Memory Wall with Optimal Tensor Rematerialization
Modern neural networks are increasingly bottlenecked by the limited capa...
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04/19/2019
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
LiDAR (Light Detection And Ranging) is an essential and widely adopted s...
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09/14/2017
ImageNet Training in Minutes
Finishing 90-epoch ImageNet-1k training with ResNet-50 on a NVIDIA M40 G...
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02/24/2016
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Recent research on deep neural networks has focused primarily on improvi...
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10/07/2015
