Search Results for author:
Found 26 papers, 6 papers with code
Rina: Enhancing Ring-AllReduce with In-network Aggregation in Distributed Model Training
The emerging In-network Aggregation (INA) has been proposed to integrate with PS to mitigate its incast issue.
FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning
This comparative analysis revealed that due to the limited information contained within client models from other clients during the initial stages of federated learning, more substantial constraints need to be imposed on the parameters of the adaptive algorithm.
StoryTTS: A Highly Expressive Text-to-Speech Dataset with Rich Textual Expressiveness Annotations
While acoustic expressiveness has long been studied in expressive text-to-speech (ETTS), the inherent expressiveness in text lacks sufficient attention, especially for ETTS of artistic works.
SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning
Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these domains.
Are LLMs Rational Investors? A Study on Detecting and Reducing the Financial Bias in LLMs
Results show varying degrees of financial irrationality among models, influenced by their design and training.
$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL
While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural language to Graph Query Language (NL2GQL) encounters hurdles due to the distinct nature of GQL compared to SQL, alongside the diverse forms of GQL.
Expressive TTS Driven by Natural Language Prompts Using Few Human Annotations
The LLM selects the best-matching style references from annotated utterances based on external style prompts, which can be raw input text or natural language style descriptions.
OSP: Boosting Distributed Model Training with 2-stage Synchronization
However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively.
DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech
Although high-fidelity speech can be obtained for intralingual speech synthesis, cross-lingual text-to-speech (CTTS) is still far from satisfactory as it is difficult to accurately retain the speaker timbres(i. e. speaker similarity) and eliminate the accents from their first language(i. e. nativeness).
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts.
Ranked #1 on
Compressed Image Super-resolution
on DIV2K-q40-x4
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.
Comprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing
For the purpose of in situ quality control, the process parameters should be controlled in real-time based on sensed information from the process, in particular the molten pool.
Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data
To enable in situ quality monitoring of bead geometry and characterization properties, we need to continuously monitor the sensor's data for molten pool dimensions and temperature for the Wire-feed laser additive manufacturing (WLAM) system.
A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing
To address the first problem, a physics-informed, data-driven model (PIM), which instead of directly using machine setting parameters to predict porosity levels of printed parts, it first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure.
BIG-bench Machine Learning
Physics-informed machine learning
FAN: Frequency Aggregation Network for Real Image Super-resolution
Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020.
LIRA: Lifelong Image Restoration from Unknown Blended Distortions
Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real distorted image that is degraded by multiple distortions.
Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration
Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations.
VESR-Net: The Winning Solution to Youku Video Enhancement and Super-Resolution Challenge
This paper introduces VESR-Net, a method for video enhancement and super-resolution (VESR).
Region Normalization for Image Inpainting
In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.
Progressive Image Inpainting with Full-Resolution Residual Network
Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes.
ZstGAN: An Adversarial Approach for Unsupervised Zero-Shot Image-to-Image Translation
Image-to-image translation models have shown remarkable ability on transferring images among different domains.
Exploring Explicit Domain Supervision for Latent Space Disentanglement in Unpaired Image-to-Image Translation
After pre-training, this network is used to extract the domain-specific features of each image.
Distribution Discrepancy Maximization for Image Privacy Preserving
Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed image from the encrypted image, and an encryption model $G_e$ is required to generate these two kinds of images to maximize the recognition rate of $D$, leading to the same training objective for both $D$ and $G_e$.
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos.
