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08/20/2023
HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
Spiking neural networks (SNNs) offer promise for efficient and powerful ...
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06/30/2023
Statler: State-Maintaining Language Models for Embodied Reasoning
Large language models (LLMs) provide a promising tool that enable robots...
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05/26/2023
Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU
This study introduces the Tempotron, a powerful classifier based on a th...
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05/04/2023
High-dimensional Bayesian Optimization via Semi-supervised Learning with Optimized Unlabeled Data Sampling
Bayesian optimization (BO) is a powerful tool for seeking the global opt...
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01/25/2023
When to Trust Aggregated Gradients: Addressing Negative Client Sampling in Federated Learning
Federated Learning has become a widely-used framework which allows learn...
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06/03/2022
CF-YOLO: Cross Fusion YOLO for Object Detection in Adverse Weather with a High-quality Real Snow Dataset
Snow is one of the toughest adverse weather conditions for object detect...
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06/02/2022
Dynamic MRI using Learned Transform-based Deep Tensor Low-Rank Network (DTLR-Net)
While low-rank matrix prior has been exploited in dynamic MR image recon...
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11/12/2021
On Transferability of Prompt Tuning for Natural Language Understanding
Prompt tuning (PT) is a promising parameter-efficient method to utilize ...
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10/05/2021
MoEfication: Conditional Computation of Transformer Models for Efficient Inference
Transformer-based pre-trained language models can achieve superior perfo...
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05/28/2021
Knowledge Inheritance for Pre-trained Language Models
Recent explorations of large-scale pre-trained language models (PLMs) su...
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05/12/2020
Making Robots Draw A Vivid Portrait In Two Minutes
Significant progress has been made with artistic robots. However, existi...
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11/04/2019
SpecuSym: Speculative Symbolic Execution for Cache Timing Leak Detection
CPU cache is limited but crucial storage on modern processor whereas the...
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11/03/2019
Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Spiking neural network is an important family of models to emulate the b...
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09/07/2019
Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View
Graph Neural Networks (GNNs) have achieved promising performance on a wi...
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08/18/2019
Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks
Spiking neural networks (SNNs) are more biologically plausible than conv...
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04/20/2019
CleanML: A Benchmark for Joint Data Cleaning and Machine Learning [Experiments and Analysis]
It is widely recognized that the data quality affects machine learning (...
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09/13/2018
