Using unlabeled data in deep active learning improves image classification accuracy.

Oriane Siméoni, Mateusz Budnik, Yannis Avrithis et al.

Proposes Graph Neural Ordinary Differential Equations (GDE), integrating GNNs with ODE solvers for continuous-depth modeling, improving static and dynamic tasks.

Michael Poli, Stefano Massaroli, Junyoung Park et al.

Proposed ROCC method improves multi-hop QA accuracy, achieving 56.82% F1 on ARC dataset.

Vikas Yadav, Steven Bethard, Mihai Surdeanu

MoCo introduces a large-scale contrastive learning framework with a momentum-updated encoder and a dynamic queue, achieving 60.6% top-1 accuracy on ImageNet with ResNet-50.

Kaiming He, Haoqi Fan, Yuxin Wu et al.

Compressive Transformer achieves state-of-the-art results on WikiText-103 and Enwik8 with 17.1 perplexity and 0.97 bpc.

Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar et al.

EVF employs fast experience encoding within a hierarchical Bayesian framework to enable rapid adaptation in visual prediction of novel objects, reducing prediction error by 15%.

Lin Yen-Chen, Maria Bauza, Phillip Isola

Noisy Student combines large models and noise injection to boost ImageNet accuracy to 88.4%, surpassing previous SOTA with fewer unlabeled images.

Qizhe Xie, Minh-Thang Luong, Eduard Hovy et al.

Introduces stabilized greedy kernel algorithms with proven convergence, stability, and uniform point distribution, applicable to Sobolev kernels.

Tizian Wenzel, Gabriele Santin, Bernard Haasdonk

BP-Transformer uses binary partitioning to efficiently model long-range context, improving text classification and translation performance.

Zihao Ye, Qipeng Guo, Quan Gan et al.

Proposed span-based multi-sentence argument linking model achieving 68.3% F1 on RAMS, leveraging a new large-scale dataset.

Seth Ebner, Patrick Xia, Ryan Culkin et al.

Proposes a Mixture-of-Experts Variational Autoencoder (MMVAE) for multi-modal generative modeling, achieving four key criteria with state-of-the-art results.

Yuge Shi, N. Siddharth, Brooks Paige et al.

TechQA dataset uses real IBM forum questions and Technote answers to advance domain adaptation research.

Vittorio Castelli, Rishav Chakravarti, Saswati Dana et al.

Introduces kernel mean matching-based instance weighting to reduce distribution shift, significantly improving transfer learning performance.

Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan et al.

pyannote.audio: An open-source toolkit for speaker diarization using PyTorch, achieving state-of-the-art performance.

Hervé Bredin, Ruiqing Yin, Juan Manuel Coria et al.

Proposes SOK framework integrating URL, web, email detection with security challenges, enhancing phishing identification.

Avisha Das, Shahryar Baki, Ayman El Aassal et al.

Constructed PrivacyQA dataset with 1750 questions; BERT-based models achieved 62.6% F1 in privacy policy QA.

Abhilasha Ravichander, Alan W Black, Shomir Wilson et al.

Large-scale GPT-based DialoGPT trained on 1.47B Reddit exchanges achieves near-human performance in open-domain dialogue.

Yizhe Zhang, Siqi Sun, Michel Galley et al.

CCNet pipeline automates extraction of high-quality monolingual datasets from Common Crawl, covering 174 languages with 532 billion tokens, boosting multilingual NLP.

Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau et al.

DD-PPO achieves near-linear scaling with 128 GPUs, training 2.5 billion steps to nearly solve PointGoal navigation.

Erik Wijmans, Abhishek Kadian, Ari Morcos et al.

Proposes a CNN-based approach with multi-scale and dilated convolutions, achieving state-of-the-art cover song identification with high robustness and efficiency.

Zhesong Yu, Xiaoshuo Xu, Xiaoou Chen et al.