Search Results for author:
Found 10 papers, 4 papers with code
Graph Network Surrogate Model for Subsurface Flow Optimization
Results are presented for a large set of test cases, in which five injection wells and five production wells are placed randomly throughout the model, with a random control variable (bottom-hole pressure) assigned to each well.
Unsupervised Temporal Action Localization via Self-paced Incremental Learning
Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance.
Evaluation and Analysis of Hallucination in Large Vision-Language Models
In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework.
Beyond Universal Transformer: block reusing with adaptor in Transformer for automatic speech recognition
To overcome the drawback of universal Transformer models for the application of ASR on edge devices, we propose a solution that can reuse the block in Transformer models for the occasion of the small footprint ASR system, which meets the objective of accommodating resource limitations without compromising recognition accuracy.
Ranked #11 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Label Information Bottleneck for Label Enhancement
In this work, we focus on the challenging problem of Label Enhancement (LE), which aims to exactly recover label distributions from logical labels, and present a novel Label Information Bottleneck (LIB) method for LE.
A Graph Neural Network Framework for Grid-Based Simulation
Reservoir simulations are computationally expensive in the well control and well placement optimization.
Use of low-fidelity models with machine-learning error correction for well placement optimization
In this work, we present an optimization framework in which these simulations are performed with low-fidelity (LF) models.
Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint.
Frame-wise Cross-modal Matching for Video Moment Retrieval
Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.
