Search Results for author:
Found 28 papers, 5 papers with code
Knowledge-Guided Paraphrase Identification
We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model.
AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models
(ii) We propose a simple merging mechanism to average the weights of multiple adapter components to collapse to a single adapter in each Transformer layer, thereby, keeping the overall parameters also the same but with significant performance improvement.
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
While the optimization problem is nonconvex and nonsmooth, we show that its critical points still have good statistical performance on the tensor completion problem.
Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification
Short text classification is a fundamental task in natural language processing.
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners
The first is the use of self-training to leverage large amounts of unlabeled data for prompt-based FN in few-shot settings.
Exploring the Common Principal Subspace of Deep Features in Neural Networks
Specifically, we design a new metric $\mathcal{P}$-vector to represent the principal subspace of deep features learned in a DNN, and propose to measure angles between the principal subspaces using $\mathcal{P}$-vectors.
FedTriNet: A Pseudo Labeling Method with Three Players for Federated Semi-supervised Learning
In particular, we propose to use three networks and a dynamic quality control mechanism to generate high-quality pseudo labels for unlabeled data, which are added to the training set.
Learning from Language Description: Low-shot Named Entity Recognition via Decomposed Framework
In this work, we study the problem of named entity recognition (NER) in a low resource scenario, focusing on few-shot and zero-shot settings.
FedCon: A Contrastive Framework for Federated Semi-Supervised Learning
Most existing FedSSL methods focus on the classical scenario, i. e, the labeled and unlabeled data are stored at the client side.
Property-Aware Relation Networks for Few-Shot Molecular Property Prediction
the target property, such that the limited labels can be effectively propagated among similar molecules.
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods.
Empirical Studies on the Convergence of Feature Spaces in Deep Learning
While deep learning is effective to learn features/representations from data, the distributions of samples in feature spaces learned by various architectures for different training tasks (e. g., latent layers of AEs and feature vectors in CNN classifiers) have not been well-studied or compared.
Adaptive Self-training for Neural Sequence Labeling with Few Labels
Neural sequence labeling is an important technique employed for many Natural Language Processing (NLP) tasks, such as Named Entity Recognition (NER), slot tagging for dialog systems and semantic parsing.
FedSiam: Towards Adaptive Federated Semi-Supervised Learning
In this paper, we focus on designing a general framework FedSiam to tackle different scenarios of federated semi-supervised learning, including four settings in the labels-at-client scenario and two setting in the labels-at-server scenario.
A Benchmark Dataset for Understandable Medical Language Translation
In this paper, we introduce MedLane -- a new human-annotated Medical Language translation dataset, to align professional medical sentences with layperson-understandable expressions.
Adaptive Self-training for Few-shot Neural Sequence Labeling
While self-training serves as an effective mechanism to learn from large amounts of unlabeled data -- meta-learning helps in adaptive sample re-weighting to mitigate error propagation from noisy pseudo-labels.
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning
To tackle this challenging task, we propose a cross-graph representation learning framework, i. e., CrossVal, which can leverage an external KG to validate the facts in the target KG efficiently.
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
Extensive low-rank matrix completion experiments on a number of synthetic and real-world data sets show that the proposed method obtains state-of-the-art recovery performance while being the fastest in comparison to existing low-rank matrix learning methods.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
(1) We formalize the problem of validating the textual attribute values of products from a variety of categories as a natural language inference task in the few-shot learning setting, and propose a meta-learning latent variable model to jointly process the signals obtained from product profiles and textual attribute values.
Decomposed Adversarial Learned Inference
Effective inference for a generative adversarial model remains an important and challenging problem.
Weak Supervision for Fake News Detection via Reinforcement Learning
In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small.
General Convolutional Sparse Coding with Unknown Noise
However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic.
AIM: Adversarial Inference by Matching Priors and Conditionals
We propose a novel approach, Adversarial Inference by Matching priors and conditionals (AIM), which explicitly matches prior and conditional distributions in both data and code spaces, and puts a direct constraint on the dependency structure of the generative model.
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events.
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing.
Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data.
