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Found 17 papers, 6 papers with code
Enabling Fast 2-bit LLM on GPUs: Memory Alignment and Asynchronous Dequantization
Weights are quantized by groups, while the ranges of weights are large in some groups, resulting in large quantization errors and nonnegligible accuracy loss (e. g. >3% for Llama2-7b with 2-bit quantization in GPTQ and Greenbit).
Estimating Effects of Long-Term Treatments
We describe the identification assumptions, the estimation strategies, and the inference technique under this framework.
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.
