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08/16/2023
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
This paper focuses on addressing the practical yet challenging problem o...
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08/08/2023
Gentopia: A Collaborative Platform for Tool-Augmented LLMs
Augmented Language Models (ALMs) empower large language models with the ...
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07/24/2023
Rethinking Data Distillation: Do Not Overlook Calibration
Neural networks trained on distilled data often produce over-confident o...
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07/19/2023
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Rare categories abound in a number of real-world networks and play a piv...
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07/01/2023
AutoST: Training-free Neural Architecture Search for Spiking Transformers
Spiking Transformers have gained considerable attention because they ach...
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04/24/2023
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Biologically inspired Spiking Neural Networks (SNNs) have attracted sign...
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03/21/2023
Time Series Contrastive Learning with Information-Aware Augmentations
Various contrastive learning approaches have been proposed in recent yea...
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02/27/2023
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate th...
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02/18/2023
Calibrating the Rigged Lottery: Making All Tickets Reliable
Although sparse training has been successfully used in various resource-...
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01/09/2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Despite impressive performance on a wide variety of tasks, deep neural n...
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11/30/2022
Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off
Over-parameterization of deep neural networks (DNNs) has shown high pred...
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11/21/2022
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Large-scale Transformer models bring significant improvements for variou...
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11/15/2022
A Survey for Efficient Open Domain Question Answering
Open domain question answering (ODQA) is a longstanding task aimed at an...
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07/16/2022
S4: a High-sparsity, High-performance AI Accelerator
Exploiting sparsity underlying neural networks has become one of the mos...
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06/21/2022
An Automatic and Efficient BERT Pruning for Edge AI Systems
With the yearning for deep learning democratization, there are increasin...
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01/29/2022
AutoDistil: Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models
Knowledge distillation (KD) methods compress large models into smaller s...
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10/28/2021
InfoGCL: Information-Aware Graph Contrastive Learning
Various graph contrastive learning models have been proposed to improve ...
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10/15/2021
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm
Various pruning approaches have been proposed to reduce the footprint re...
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04/18/2021
Rethinking Network Pruning – under the Pre-train and Fine-tune Paradigm
Transformer-based pre-trained language models have significantly improve...
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11/09/2020
Parameterized Explainer for Graph Neural Network
Despite recent progress in Graph Neural Networks (GNNs), explaining pred...
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05/24/2020
Longitudinal Deep Kernel Gaussian Process Regression
We consider the problem of learning predictive models from longitudinal ...
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11/11/2019
LMLFM: Longitudinal Multi-Level Factorization Machines
Selecting important variables and learning predictive models from high-d...
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11/11/2019
LMLFM: Longitudinal Multi-Level Factorization Machine
We consider the problem of learning predictive models from longitudinal ...
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12/12/2016