A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
Yijiang Li, Bingyang Wang, Yijun Liang, et al.
Proposes U-OPSD, an unsupervised on-policy self-distillation method that uses only a model's own generations and majority voting to improve reasoning without external supervision.
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This paper introduces Make-a-video, a method for text-to-video generation that leverages text-to-image models and unsupervised video data, achieving state-of-the-art results without paired text-video data.
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This paper introduces a training framework for real-time hallucination detection in LLMs using internal states, along with the HELM benchmark for evaluation.
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This paper introduces unsupervised process reward models that generate step-level supervision for LLM reasoning without human labels, improving performance on math reasoning tasks.
Paul Bergmann, Kilian Batzner, Michael Fauser, et al.
Introduces MVTec AD, a comprehensive dataset with 5354 high-resolution images across 15 categories for unsupervised anomaly detection, and benchmarks state-of-the-art methods.
Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, et al.
DeepAnT uses a CNN-based predictor and unsupervised anomaly detector to detect point, contextual, and discord anomalies in time series without requiring labeled data.
Ichiro Tsuda, Edgar Koerner, Hideki Shimizu
This paper presents an asynchronous neural network model with field-effect feedback that enables unsupervised learning and deterministic chaotic successive memory recall.
Nathan Shone, Trần Nguyên Ngọc, Vu Dinh Phai, et al.
Proposes a nonsymmetric deep autoencoder (NDAE) for unsupervised feature learning and a stacked NDAE classifier for network intrusion detection, evaluated on KDD Cup '99 and NSL-KDD datasets.
Riccardo Miotto, Li Li, Brian Kidd, et al.
Unsupervised deep feature learning with stacked denoising autoencoders on EHRs of ~700k patients yields a general-purpose patient representation that significantly improves predictive modeling for 78 diseases.
Supriya V. Mahadevkar, Bharti Khemani, Shruti Patil, et al.
A systematic review of machine learning styles in computer vision, covering supervised, unsupervised, reinforcement, and emerging techniques like zero-shot and self-supervised learning.
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SCPO uses self-consistency to generate preference pairs for unsupervised LLM training, improving reasoning without human labels.
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SimCLRv2 presents a semi-supervised learning framework combining unsupervised pretraining, supervised fine-tuning, and distillation with unlabeled data.