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Found 19 papers, 5 papers with code
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.
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.
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).
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 #4 on
Natural Language Inference
on CommitmentBank
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
Improving Longer-range Dialogue State Tracking
Dialogue state tracking (DST) is a pivotal component in task-oriented dialogue systems.
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.
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.
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.
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.
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.
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.
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.
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.
Active Discriminative Text Representation Learning
We also show that, as expected, the method quickly learns discriminative word embeddings.
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.
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.
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).
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.
