Search Results for author:
Found 21 papers, 6 papers with code
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
We compare the performance of the same optimization methods across different hardware platforms, the performance across different hardware platforms, and the performance of different methods on the same hardware platform.
MARCA: Mamba Accelerator with ReConfigurable Architecture
We propose intra-operation buffer management strategy to maximize input data sharing for linear operations within operations, and inter-operation strategy for element-wise operations between operations.
"The Strength of Weak Ties" Varies Across Viral Channels
Furthermore, the "strength of weak ties" is only evident in BC, not in DM where weaker ties do not transmit significantly more novel information.
Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations
In the rapidly evolving field of natural language processing, dialogue systems primarily employ a single-step dialogue paradigm.
Fast and Efficient 2-bit LLM Inference on GPU: 2/4/16-bit in a Weight Matrix with Asynchronous Dequantization
To tackle these challenges and enable fast and efficient LLM inference on GPUs, we propose the following techniques in this paper.
Estimating Effects of Long-Term Treatments
Estimating the effects of long-term treatments through A/B testing is challenging.
TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
We perform a generative task to encapsulate diverse priors into a generative network (GAN), which is the decoder sub-module of the deep non-local feature capture part, in the first stage.
A Novel Interpretable and Generalizable Re-synchronization Model for Cued Speech based on a Multi-Cuer Corpus
Cued Speech (CS) is a multi-modal visual coding system combining lip reading with several hand cues at the phonetic level to make the spoken language visible to the hearing impaired.
Federated Learning for Metaverse: A Survey
In this paper, we review some of the early advances of FL4M, which will be a research direction with unlimited development potential.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Contrastive Credibility Propagation for Reliable Semi-Supervised Learning
CCP unifies semi-supervised learning and noisy label learning for the goal of reliably outperforming a supervised baseline in any data scenario.
Dynamic optical contrast imaging for real-time delineation of tumor resection margins using head and neck cancer as a model
Complete surgical resection of the tumor for Head and neck squamous cell carcinoma (HNSCC) remains challenging, given the devastating side effects of aggressive surgery and the anatomic proximity to vital structures.
WebUAV-3M: A Benchmark for Unveiling the Power of Million-Scale Deep UAV Tracking
In this work, we propose WebUAV-3M, the largest public UAV tracking benchmark to date, to facilitate both the development and evaluation of deep UAV trackers.
Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning
Deep Reinforcement learning is a branch of unsupervised learning in which an agent learns to act based on environment state in order to maximize its total reward.
Two Stochastic Control Problems In Capital Structure and Portfolio Choice
In the first problem, we derive a stochastic control model to optimize banks' dividend and recapitalization policies and calibrate that to a sample of U. S. banks in the situation where we model banks' true accounting asset values as partially observed variables due to the opaqueness in banks' assets.
DeepLM: Large-Scale Nonlinear Least Squares on Deep Learning Frameworks Using Stochastic Domain Decomposition
We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks.
Emotions in Online Content Diffusion
We apply a partial-linear instrumental variable approach with a double machine learning framework to causally identify the impact of the negative discrete emotions on online content diffusion.
Predicting nucleation near the spinodal in the Ising model using machine learning
We use a Convolutional Neural Network (CNN) and two logistic regression models to predict the probability of nucleation in the two-dimensional Ising model.
A Study of Pyramid Structure for Code Correction
We demonstrate the implementations of pyramid encoders in both multi-layer GRU and Transformer for seq2seq tasks.
Software Engineering
A Multi-oriented Chinese Keyword Spotter Guided by Text Line Detection
Chinese keyword spotting is a challenging task as there is no visual blank for Chinese words.
Taxable Stock Trading with Deep Reinforcement Learning
In this paper, we propose stock trading based on the average tax basis.
