Search Results for author:
Found 23 papers, 10 papers with code
Sketchy Moment Matching: Toward Fast and Provable Data Selection for Finetuning
We revisit data selection in a modern context of finetuning from a fundamental perspective.
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Bridging Domains with Approximately Shared Features
Under our framework, we design and analyze a learning procedure consisting of learning approximately shared feature representation from source tasks and fine-tuning it on the target task.
Transferable Learned Image Compression-Resistant Adversarial Perturbations
Adversarial attacks can readily disrupt the image classification system, revealing the vulnerability of DNN-based recognition tasks.
Corner-to-Center Long-range Context Model for Efficient Learned Image Compression
To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding.
PostRainBench: A comprehensive benchmark and a new model for precipitation forecasting
To address these limitations, we introduce the PostRainBench, a comprehensive multi-variable NWP post-processing benchmark consisting of three datasets for NWP post-processing-based precipitation forecasting.
Reconstruction Distortion of Learned Image Compression with Imperceptible Perturbations
Learned Image Compression (LIC) has recently become the trending technique for image transmission due to its notable performance.
Accelerating Dataset Distillation via Model Augmentation
Dataset Distillation (DD), a newly emerging field, aims at generating much smaller but efficient synthetic training datasets from large ones.
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens
In case (i), we want the model to be invariant to the feature, which is neither necessary nor sufficient for prediction.
Task Transfer and Domain Adaptation for Zero-Shot Question Answering
Pretrained language models have shown success in various areas of natural language processing, including reading comprehension tasks.
DeepOPF-AL: Augmented Learning for Solving AC-OPF Problems with Multiple Load-Solution Mappings
The existence of multiple load-solution mappings of non-convex AC-OPF problems poses a fundamental challenge to deep neural network (DNN) schemes.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
We systematically calibrate inequality constraints used in DNN training, thereby anticipating prediction errors and ensuring the resulting solutions remain feasible.
DataCLUE: A Benchmark Suite for Data-centric NLP
However, we have not seen significant research progress in this field, especially in NLP.
Calculating Question Similarity is Enough: A New Method for KBQA Tasks
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base.
FewCLUE: A Chinese Few-shot Learning Evaluation Benchmark
While different learning schemes -- fine-tuning, zero-shot, and few-shot learning -- have been widely explored and compared for languages such as English, there is comparatively little work in Chinese to fairly and comprehensively evaluate and compare these methods and thus hinders cumulative progress.
DeepOPF-V: Solving AC-OPF Problems Efficiently
AC optimal power flow (AC-OPF) problems need to be solved more frequently in the future to maintain stable and economic power system operation.
Multi-Granularity Modularized Network for Abstract Visual Reasoning
Abstract visual reasoning connects mental abilities to the physical world, which is a crucial factor in cognitive development.
Improving auto-encoder novelty detection using channel attention and entropy minimization
Novelty detection is a important research area which mainly solves the classification problem of inliers which usually consists of normal samples and outliers composed of abnormal samples.
DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems
High percentage penetrations of renewable energy generations introduce significant uncertainty into power systems.
DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow
We then directly reconstruct the phase angles from the generations and loads by using the power flow equations.
DeepOPF: Deep Neural Network for DC Optimal Power Flow
DeepOPF is inspired by the observation that solving DC-OPF for a given power network is equivalent to characterizing a high-dimensional mapping between the load inputs and the dispatch and transmission decisions.
Systems and Control
