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- EnlightenGAN: Deep Light Enhancement Without Paired Supervision2,715
This paper proposes a highly effective unsupervised generative adversarial network, dubbed EnlightenGAN, that can be trained without low/normal-light image pairs, yet proves to generalize very well on various real-world test images.
- Patient Knowledge Distillation for BERT Model Compression1,021
This work proposes a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student), which translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy.
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- Risk Prediction with Electronic Health Records: A Deep Learning Approach433
A deep learning approach for phenotyping from patient EHRs by building a four-layer convolutional neural network model for extracting phenotypes and perform prediction and the proposed model is validated on a real world EHR data warehouse under the scenario of predictive modeling of chronic diseases.
- Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification410
Evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.
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- Jointly Attentive Spatial-Temporal Pooling Networks for Video-Based Person Re-identification341
This work presents a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching items can directly influence the computation of each other's representation.
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- Diverse Few-Shot Text Classification with Multiple Metrics228
This work proposes an adaptive metric learning approach that automatically determines the best weighted combination from a set of metrics obtained from meta-training tasks for a newly seen few-shot task.
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- Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy139
M-SMoE is proposed, which leverages routing statistics to guide expert merging and further decomposes the merged experts into low-rank and structural sparse alternatives, thus diminishing the impact of insignificant experts.
- Multi-Fact Correction in Abstractive Text Summarization135
Span-Fact is a suite of two factual correction models that leverages knowledge learned from question answering models to make corrections in system-generated summaries via span selection to significantly boost the factual consistency of system- generated summaries without sacrificing summary quality in terms of both automatic metrics and human evaluation.
- Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding122
A patient similarity evaluation framework based on temporal matching of longitudinal patient EHRs, which takes a convolutional neural network architecture, and learns an optimal representation of patient clinical record through medical concept embedding.
- Measuring Patient Similarities via a Deep Architecture with Medical Concept Embedding122
A patient similarity evaluation framework based on temporal matching of longitudinal patient EHRs, which takes a convolutional neural network architecture, and learns an optimal representation of patient clinical record through medical concept embedding.
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- Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data116
This work tracks the faces of casual walkers on more than 40 hours of egocentric video and automatically extracts nearly 5 million pairs of images connected by or from different face tracks, along with their weather and location context, under pose and lighting variations, to learn a rich feature representation for facial attribute classification.
- EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets113
By slimming the self-attention and fully-connected sub-layers inside a transformer, this work is the first to identify structured winning tickets in the early stage of BERT training.
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- Contrastive Distillation on Intermediate Representations for Language Model Compression87
CoDIR is proposed, a principled knowledge distillation framework where the student is trained to distill knowledge through intermediate layers of the teacher via a contrastive objective, and achieves superb performance on the GLUE benchmark, outperforming state-of-the-art compression methods.
- Mining millions of reviews85
A product ranking model that applies weights to product review factors to calculate a products ranking score is presented which produces product ranking results that closely relate to the products sales ranking as reported by the retailer.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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