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Found 10 papers, 8 papers with code
Training Discrete Energy-Based Models with Energy Discrepancy
Training energy-based models (EBMs) on discrete spaces is challenging because sampling over such spaces can be difficult.
Energy Discrepancies: A Score-Independent Loss for Energy-Based Models
Energy-based models are a simple yet powerful class of probabilistic models, but their widespread adoption has been limited by the computational burden of training them.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
Modeling Semantic Composition with Syntactic Hypergraph for Video Question Answering
A key challenge in video question answering is how to realize the cross-modal semantic alignment between textual concepts and corresponding visual objects.
Learning Neural Set Functions Under the Optimal Subset Oracle
Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery.
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation
In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query.
Refining BERT Embeddings for Document Hashing via Mutual Information Maximization
As a first try, we modify existing generative hashing models to accommodate the BERT embeddings.
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval
With the need of fast retrieval speed and small memory footprint, document hashing has been playing a crucial role in large-scale information retrieval.
Unsupervised Hashing with Contrastive Information Bottleneck
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible.
Imperfect also Deserves Reward: Multi-Level and Sequential Reward Modeling for Better Dialog Management
For task-oriented dialog systems, training a Reinforcement Learning (RL) based Dialog Management module suffers from low sample efficiency and slow convergence speed due to the sparse rewards in RL. To solve this problem, many strategies have been proposed to give proper rewards when training RL, but their rewards lack interpretability and cannot accurately estimate the distribution of state-action pairs in real dialogs.
