Search Results for author:
Found 13 papers, 6 papers with code
HiQA: A Hierarchical Contextual Augmentation RAG for Massive Documents QA
As language model agents leveraging external tools rapidly evolve, significant progress has been made in question-answering(QA) methodologies utilizing supplementary documents and the Retrieval-Augmented Generation (RAG) approach.
Federated Deep Multi-View Clustering with Global Self-Supervision
Second, the storage and usage of data from multiple clients in a distributed environment can lead to incompleteness of multi-view data.
Revisiting the Role of Language Priors in Vision-Language Models
Our first observation is that they can be repurposed for discriminative tasks (such as image-text retrieval) by simply computing the match score of generating a particular text string given an image.
Ranked #45 on
Visual Reasoning
on Winoground
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks
In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
Rethinking and Recomputing the Value of ML Models
In this paper, we argue that the way we have been training and evaluating ML models has largely forgotten the fact that they are applied in an organization or societal context as they provide value to people.
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
In particular, Truncated Quantile Critics (TQC) achieves state-of-the-art asymptotic training performance on the MuJoCo benchmark with a distributional representation of critics; and Randomized Ensemble Double Q-Learning (REDQ) achieves high sample efficiency that is competitive with state-of-the-art model-based methods using a high update-to-data ratio and target randomization.
Multi-Frequency Wireless Channel Measurements and Characteristics Analysis in Indoor Corridor Scenarios
In this paper, we conduct wireless channel measurements in indoor corridor scenarios at 2. 4, 5 and 6 GHz bands with bandwidth of 320 MHz.
Remote Sensing Change Detection Based on Multidirectional Adaptive Feature Fusion and Perceptual Similarity
In this paper we propose a novel multidirectional fusion and perception network for change detection in bi-temporal very-high-resolution remote sensing images.
Change Detection
Change detection for remote sensing images
+1
Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
Existing work on augmenting question answering (QA) models with external knowledge (e. g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale.
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional interactions with the environment.
KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
Commonsense reasoning aims to empower machines with the human ability to make presumptions about ordinary situations in our daily life.
Ranked #29 on
Common Sense Reasoning
on CommonsenseQA
(using extra training data)
