Search Results for author:
Found 33 papers, 10 papers with code
Generating ordered list of Recommended Items: a Hybrid Recommender System of Microblog
Precise recommendation of followers helps in improving the user experience and maintaining the prosperity of twitter and microblog platforms.
A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks (CNNs) have recently achieved remarkably strong performance on the practically important task of sentence classification (kim 2014, kalchbrenner 2014, johnson 2014).
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification
We introduce a novel, simple convolution neural network (CNN) architecture - multi-group norm constraint CNN (MGNC-CNN) that capitalizes on multiple sets of word embeddings for sentence classification.
Rationale-Augmented Convolutional Neural Networks for Text Classification
We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their component sentences.
Active Discriminative Text Representation Learning
We also show that, as expected, the method quickly learns discriminative word embeddings.
Neural Information Retrieval: A Literature Review
A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing.
Exploiting Domain Knowledge via Grouped Weight Sharing with Application to Text Categorization
A fundamental advantage of neural models for NLP is their ability to learn representations from scratch.
SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation
Supervised training of abstractive language generation models results in learning conditional probabilities over language sequences based on the supervised training signal.
AceKG: A Large-scale Knowledge Graph for Academic Data Mining
Most existing knowledge graphs (KGs) in academic domains suffer from problems of insufficient multi-relational information, name ambiguity and improper data format for large-scale machine processing.
Do Human Rationales Improve Machine Explanations?
Work on "learning with rationales" shows that humans providing explanations to a machine learning system can improve the system's predictive accuracy.
Discrimination in the Venture Capital Industry: Evidence from Field Experiments
Investors implicitly discriminate against female and Asian founders when evaluating attractive startups, but they favor female and Asian founders when evaluating struggling startups.
Estimating Adsorption Isotherm Parameters in Chromatography via a Virtual Injection Promoting Feed-forward Neural Network
The means to obtain the adsorption isotherms is a fundamental open problem in competitive chromatography.
Improving Longer-range Dialogue State Tracking
Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems.
Effective Sequence-to-Sequence Dialogue State Tracking
We also explore using Pegasus, a span prediction-based pre-training objective for text summarization, for the state tracking model.
Ranked #1 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.4
N-Grammer: Augmenting Transformers with latent n-grams
Transformer models have recently emerged as one of the foundational models in natural language processing, and as a byproduct, there is significant recent interest and investment in scaling these models.
Ranked #7 on
Language Modelling
on C4
A new Speech Feature Fusion method with cross gate parallel CNN for Speaker Recognition
In this paper, a new speech feature fusion method is proposed for speaker recognition on the basis of the cross gate parallel convolutional neural network (CG-PCNN).
A Novel Speech Feature Fusion Algorithm for Text-Independent Speaker Recognition
To estimate the IFCs, the TD and the FD features of the speaker's speech are concatenated to build the TD and the FD feature matrix, respectively.
CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome.
Are words equally surprising in audio and audio-visual comprehension?
We report a controlled study investigating the effect of visual information (i. e., seeing the speaker) on spoken language comprehension.
A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition
In recent years, facial expression recognition (FER) has garnered significant attention within the realm of computer vision research.
Ranked #1 on
Facial Expression Recognition (FER)
on AffectNet
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
Graph-based Asynchronous Event Processing for Rapid Object Recognition
Different from traditional video cameras, event cameras capture asynchronous events stream in which each event encodes pixel location, trigger time, and the polarity of the brightness changes.
Multi-level feature fusion network combining attention mechanisms for polyp segmentation
Firstly, features extracted by encoders are not adequately filtered and utilized.
Gemini: A Family of Highly Capable Multimodal Models
This report introduces a new family of multimodal models, Gemini, that exhibit remarkable capabilities across image, audio, video, and text understanding.
Ranked #1 on
Multi-task Language Understanding
on MMLU
(using extra training data)
SEINE: Structure Encoding and Interaction Network for Nuclei Instance Segmentation
Concretely, SEINE introduces a contour-based structure encoding (SE) that considers the correlation between nuclei structure and semantics, realizing a reasonable representation of the nuclei structure.
DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans
Gastrointestinal (GI) tract cancers pose a global health challenge, demanding precise radiotherapy planning for optimal treatment outcomes.
Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
The model has two key designs: a low-resolution denoising (LRD) module and a cross-RoI contrastive learning (CRC) module.
Neural Graphics Primitives-based Deformable Image Registration for On-the-fly Motion Extraction
Intra-fraction motion in radiotherapy is commonly modeled using deformable image registration (DIR).
One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble
In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization.
Development and Application of a Monte Carlo Tree Search Algorithm for Simulating Da Vinci Code Game Strategies
In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted.
Generative AI for Architectural Design: A Literature Review
This paper explores the extensive applications of generative AI technologies in architectural design, a trend that has benefited from the rapid development of deep generative models.
Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT Synthesis for Head and Neck Proton Treatment Planning
The effectiveness of DSBM in MR-based proton treatment planning highlights its potential as a valuable tool in various clinical scenarios.
DAWN: Domain-Adaptive Weakly Supervised Nuclei Segmentation via Cross-Task Interactions
However, the current weakly supervised nuclei segmentation approaches typically follow a two-stage pseudo-label generation and network training process.
