Search Results for author:
Found 21 papers, 11 papers with code
Super-resolution imaging using super-oscillatory diffractive neural networks
Here, we propose an optical super-oscillatory diffractive neural network, i. e., SODNN, that can achieve super-resolved spatial resolution for imaging beyond the diffraction limit with superior performance over existing methods.
An algorithm for forensic toolmark comparisons
To address this, we first generate a dataset of 3D toolmarks from various angles and directions using consecutively manufactured slotted screwdrivers.
DP-HyPO: An Adaptive Private Hyperparameter Optimization Framework
In our paper, we introduce DP-HyPO, a pioneering framework for ``adaptive'' private hyperparameter optimization, aiming to bridge the gap between private and non-private hyperparameter optimization.
Dual adaptive training of photonic neural networks
Photonic neural network (PNN) is a remarkable analog artificial intelligence (AI) accelerator that computes with photons instead of electrons to feature low latency, high energy efficiency, and high parallelism.
Hybrid Multimodal Feature Extraction, Mining and Fusion for Sentiment Analysis
In this paper, we present our solutions for the Multimodal Sentiment Analysis Challenge (MuSe) 2022, which includes MuSe-Humor, MuSe-Reaction and MuSe-Stress Sub-challenges.
Analytical Composition of Differential Privacy via the Edgeworth Accountant
Many modern machine learning algorithms are composed of simple private algorithms; thus, an increasingly important problem is to efficiently compute the overall privacy loss under composition.
Sparse GCA and Thresholded Gradient Descent
Based on a Lagrangian form of the sample optimization problem, we propose a thresholded gradient descent algorithm for estimating GCA loading vectors and matrices in high dimensions.
LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction
In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules.
A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech
A second (multi-relational) GCN is then applied to the utterance states to produce a discourse relation-augmented representation for the utterances, which are then fused together with token states in each utterance as input to a dropped pronoun recovery layer.
Ranked #5 on
Discourse Parsing
on STAC
Noisy-Labeled NER with Confidence Estimation
This work studies NER under a noisy labeled setting with calibrated confidence estimation.
Improving Abstractive Dialogue Summarization with Conversational Structure and Factual Knowledge
In this paper, we present a Knowledge Graph Enhanced Dual-Copy network (KGEDC), a novel framework for abstractive dialogue summarization with conversational structure and factual knowledge.
Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields
Exploratory analysis also demonstrates that the GCRF did help to capture the dependencies between pronouns in neighboring utterances, thus contributes to the performance improvements.
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification
For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.
A Prism Module for Semantic Disentanglement in Name Entity Recognition
To solve this problem, we propose a prism module to disentangle the semantic aspects of words and reduce noise at the input layer of a model.
Ranked #51 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
Recovering Dropped Pronouns in Chinese Conversations via Modeling Their Referents
Pronouns are often dropped in Chinese sentences, and this happens more frequently in conversational genres as their referents can be easily understood from context.
Pre-training of Context-aware Item Representation for Next Basket Recommendation
In the offline phase, IERT pre-trains deep item representations conditioning on their transaction contexts.
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
Dropout is used to avoid overfitting by randomly dropping units from the neural networks during training.
Guiding Generation for Abstractive Text Summarization Based on Key Information Guide Network
Then, we introduce a Key Information Guide Network (KIGN), which encodes the keywords to the key information representation, to guide the process of generation.
Ranked #10 on
Text Summarization
on CNN / Daily Mail (Anonymized)
A Tree Search Algorithm for Sequence Labeling
Inspired by the success and methodology of the AlphaGo Zero, MM-Tag formalizes the problem of sequence tagging with a Monte Carlo tree search (MCTS) enhanced Markov decision process (MDP) model, in which the time steps correspond to the positions of words in a sentence from left to right, and each action corresponds to assign a tag to a word.
Neural Regularized Domain Adaptation for Chinese Word Segmentation
For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature.
Improving Cross-domain Recommendation through Probabilistic Cluster-level Latent Factor Model--Extended Version
Cross-domain recommendation has been proposed to transfer user behavior pattern by pooling together the rating data from multiple domains to alleviate the sparsity problem appearing in single rating domains.
