SimCSE achieves 76.3% unsupervised and 81.6% supervised Spearman's correlation on STS tasks with BERT-base, improving prior best results by 4.2% and 2.2% via simple contrastive learning.
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12 Pith papers cite this work. Polarity classification is still indexing.
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Self-improvement of a decoder-only transformer yields plans averaging 30% shorter than a source symbolic planner, over 80% optimal where known, with sub-exponential latency scaling.
LIMSSR reformulates incomplete multimodal learning as LLM-driven sequence-to-score reasoning with prompt-guided imputation and mask-aware aggregation, outperforming baselines on action quality assessment without complete training data.
HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.
A new shared video-image tokenizer enables large language models to surpass diffusion models on standard visual generation benchmarks.
IDEAL learns intrinsic deviation vectors via Normal Variation Eraser and Intrinsic Deviation Encoder to score query deviations for both seen and unseen anomalies in discriminative FSAD.
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
PTA adapts VLMs at test time by maintaining and updating class-specific knowledge prototypes from test samples, achieving higher accuracy than cache-based methods with far less speed loss.
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
Finetuning open LMs on ChatGPT outputs creates models that mimic style and fool human raters but fail to close the performance gap to proprietary systems on tasks not well-represented in the imitation data.
citing papers explorer
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SimCSE: Simple Contrastive Learning of Sentence Embeddings
SimCSE achieves 76.3% unsupervised and 81.6% supervised Spearman's correlation on STS tasks with BERT-base, improving prior best results by 4.2% and 2.2% via simple contrastive learning.
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Self-Improvement for Fast, High-Quality Plan Generation
Self-improvement of a decoder-only transformer yields plans averaging 30% shorter than a source symbolic planner, over 80% optimal where known, with sub-exponential latency scaling.
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LIMSSR: LLM-Driven Sequence-to-Score Reasoning under Training-Time Incomplete Multimodal Observations
LIMSSR reformulates incomplete multimodal learning as LLM-driven sequence-to-score reasoning with prompt-guided imputation and mask-aware aggregation, outperforming baselines on action quality assessment without complete training data.
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Hyperspherical Forward-Forward with Prototypical Representations
HFF replaces binary goodness-of-fit in Forward-Forward with hyperspherical prototypes for direct multi-class decisions, enabling single-forward-pass inference and training that scales to ImageNet while closing much of the gap to backpropagation.
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Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation
A new shared video-image tokenizer enables large language models to surpass diffusion models on standard visual generation benchmarks.
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Beyond Normal References: Discriminative Few-Shot Anomaly Detection
IDEAL learns intrinsic deviation vectors via Normal Variation Eraser and Intrinsic Deviation Encoder to score query deviations for both seen and unseen anomalies in discriminative FSAD.
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QHyer: Q-conditioned Hybrid Attention-mamba Transformer for Offline Goal-conditioned RL
QHyer replaces return-to-go with a state-conditioned Q-estimator and adds a gated hybrid attention-mamba backbone to achieve state-of-the-art performance in offline goal-conditioned RL on both Markovian and non-Markovian datasets.
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Prototype-Based Test-Time Adaptation of Vision-Language Models
PTA adapts VLMs at test time by maintaining and updating class-specific knowledge prototypes from test samples, achieving higher accuracy than cache-based methods with far less speed loss.
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PixArt-$\alpha$: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis
PixArt-α matches commercial text-to-image quality with a diffusion transformer trained in 675 A100 GPU days through decomposed training stages, cross-attention text injection, and vision-language model dense captions.
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Vision Transformers Need Registers
Adding register tokens to Vision Transformers eliminates high-norm background artifacts and raises state-of-the-art performance on dense visual prediction tasks.
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The False Promise of Imitating Proprietary LLMs
Finetuning open LMs on ChatGPT outputs creates models that mimic style and fool human raters but fail to close the performance gap to proprietary systems on tasks not well-represented in the imitation data.
- SURGE: Surrogate Gradient Adaptation in Binary Neural Networks