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Found 19 papers, 3 papers with code
VQSynery: Robust Drug Synergy Prediction With Vector Quantization Mechanism
The pursuit of optimizing cancer therapies is significantly advanced by the accurate prediction of drug synergy.
GliDe with a CaPE: A Low-Hassle Method to Accelerate Speculative Decoding
Speculative decoding is a relatively new decoding framework that leverages small and efficient draft models to reduce the latency of LLMs.
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification
Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases.
Image Classification
Semi-supervised Medical Image Classification
Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment
Medical image segmentation has made significant progress when a large amount of labeled data are available.
dugMatting: Decomposed-Uncertainty-Guided Matting
Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
Improving Robustness and Generality of NLP Models Using Disentangled Representations
We show that models trained with the proposed criteria provide better robustness and domain adaptation ability in a wide range of supervised learning tasks.
Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
At the time of writing, the ongoing pandemic of coronavirus disease (COVID-19) has caused severe impacts on society, economy and people's daily lives.
Towards Cognitive Routing based on Deep Reinforcement Learning
Routing is one of the key functions for stable operation of network infrastructure.
Learning to Learn and Predict: A Meta-Learning Approach for Multi-Label Classification
Many tasks in natural language processing can be viewed as multi-label classification problems.
TWEETQA: A Social Media Focused Question Answering Dataset
With social media becoming increasingly pop-ular on which lots of news and real-time eventsare reported, developing automated questionanswering systems is critical to the effective-ness of many applications that rely on real-time knowledge.
Self-Supervised Dialogue Learning
The sequential order of utterances is often meaningful in coherent dialogues, and the order changes of utterances could lead to low-quality and incoherent conversations.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context.
Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.
Ranked #3 on
Entity Typing
on Ontonotes v5 (English)
Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning
Although promising results have been achieved in video captioning, existing models are limited to the fixed inventory of activities in the training corpus, and do not generalize to open vocabulary scenarios.
Reinforced Co-Training
However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space.
Video Captioning via Hierarchical Reinforcement Learning
Video captioning is the task of automatically generating a textual description of the actions in a video.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
Knowledge Representation via Joint Learning of Sequential Text and Knowledge Graphs
There are two main challenges for constructing knowledge representations from plain texts: (1) How to take full advantages of sequential contexts of entities in plain texts for KRL.
