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"title": "Attention Is All You Need",
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"title": "Is Attention All What You Need? -- An Empirical Investigation on Convolution-Based Active Memory and Self-Attention",
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"title": "Positioning is All You Need",
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"arxiv_id": "2502.05383",
"title": "Is attention all you need to solve the correlated electron problem?",
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"arxiv_id": "2405.06478",
"title": "Attention is all they need: Cognitive science and the (techno)political economy of attention in humans and machines",
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"arxiv_id": "2304.04556",
"title": "Attention: Marginal Probability is All You Need?",
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"title": "Element-wise Attention Is All You Need",
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"title": "Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers",
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"title": "Attention is all you need for an improved CNN-based flash flood susceptibility modeling. The case of the ungauged Rheraya watershed, Morocco",
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"title": "Attention is All You Need Until You Need Retention",
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"query": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
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"title": "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding",
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"title": "Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquors",
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"arxiv_id": "2106.08254",
"title": "BEiT: BERT Pre-Training of Image Transformers",
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"arxiv_id": "1909.04120",
"title": "Span Selection Pre-training for Question Answering",
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"arxiv_id": "2401.15861",
"title": "BPDec: Unveiling the Potential of Masked Language Modeling Decoder in BERT pretraining",
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"arxiv_id": "1909.10351",
"title": "TinyBERT: Distilling BERT for Natural Language Understanding",
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"arxiv_id": "2011.07208",
"title": "Utilizing Bidirectional Encoder Representations from Transformers for Answer Selection",
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"arxiv_id": "1905.05583",
"title": "How to Fine-Tune BERT for Text Classification?",
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"arxiv_id": "1906.08237",
"title": "XLNet: Generalized Autoregressive Pretraining for Language Understanding",
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"rank": 10,
"arxiv_id": "2112.07571",
"title": "Epigenomic language models powered by Cerebras",
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"band": "A",
"query": "Deep Residual Learning for Image Recognition",
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"arxiv_id": "1512.03385",
"title": "Deep Residual Learning for Image Recognition",
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"rank": 2,
"arxiv_id": "1604.01335",
"title": "Deep Cross Residual Learning for Multitask Visual Recognition",
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"arxiv_id": "1805.00325",
"title": "Study of Residual Networks for Image Recognition",
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"arxiv_id": "2505.01632",
"title": "Transfer Learning-Based Deep Residual Learning for Speech Recognition in Clean and Noisy Environments",
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"rank": 5,
"arxiv_id": "2004.04989",
"title": "Improved Residual Networks for Image and Video Recognition",
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"rank": 6,
"arxiv_id": "1604.08671",
"title": "Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution",
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"rank": 7,
"arxiv_id": "1707.08866",
"title": "Deep Residual Learning for Weakly-Supervised Relation Extraction",
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"rank": 8,
"arxiv_id": "1706.08807",
"title": "Recurrent Residual Learning for Action Recognition",
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"arxiv_id": "2211.12320",
"title": "A Cross-Residual Learning for Image Recognition",
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"rank": 10,
"arxiv_id": "2107.05318",
"title": "R3L: Connecting Deep Reinforcement Learning to Recurrent Neural Networks for Image Denoising via Residual Recovery",
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{
"band": "F",
"query": "how do transformers work in NLP",
"description": "Newcomer asking about transformer basics",
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"arxiv_id": "2406.16893",
"title": "A Survey on Transformers in NLP with Focus on Efficiency",
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"arxiv_id": "1910.06764",
"title": "Stabilizing Transformers for Reinforcement Learning",
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"arxiv_id": "2204.05673",
"title": "What do Toothbrushes do in the Kitchen? How Transformers Think our World is Structured",
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"arxiv_id": "2410.13732",
"title": "Reducing the Transformer Architecture to a Minimum",
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"rank": 5,
"arxiv_id": "2306.07303",
"title": "A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks",
"category": "?"
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{
"rank": 6,
"arxiv_id": "2202.07856",
"title": "The NLP Task Effectiveness of Long-Range Transformers",
"category": "?"
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{
"rank": 7,
"arxiv_id": "2310.10930",
"title": "Enhanced Transformer Architecture for Natural Language Processing",
"category": "?"
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{
"rank": 8,
"arxiv_id": "1907.00570",
"title": "Do Transformer Attention Heads Provide Transparency in Abstractive Summarization?",
"category": "?"
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{
"rank": 9,
"arxiv_id": "1906.06755",
"title": "Theoretical Limitations of Self-Attention in Neural Sequence Models",
"category": "?"
},
{
"rank": 10,
"arxiv_id": "2105.08050",
"title": "Pay Attention to MLPs",
"category": "?"
}
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"band": "F",
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"rank": 1,
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"title": "A Survey of Reinforcement Learning from Human Feedback",
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"rank": 2,
"arxiv_id": "2504.12501",
"title": "Reinforcement Learning from Human Feedback",
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"rank": 3,
"arxiv_id": "2504.14732",
"title": "Reinforcement Learning from Multi-level and Episodic Human Feedback",
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"rank": 4,
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"title": "Sample-Efficient Reinforcement Learning from Human Feedback via Information-Directed Sampling",
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"rank": 5,
"arxiv_id": "2211.11602",
"title": "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback",
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{
"rank": 6,
"arxiv_id": "2410.02504",
"title": "Dual Active Learning for Reinforcement Learning from Human Feedback",
"category": "?"
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{
"rank": 7,
"arxiv_id": "2303.02891",
"title": "Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback",
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{
"rank": 8,
"arxiv_id": "2310.13639",
"title": "Contrastive Preference Learning: Learning from Human Feedback without RL",
"category": "?"
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{
"rank": 9,
"arxiv_id": "1707.07402",
"title": "Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback",
"category": "?"
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"rank": 10,
"arxiv_id": "1805.10627",
"title": "Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement Learning",
"category": "?"
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"band": "F",
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"title": "The training response law explains how deep neural networks learn",
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"title": "Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)",
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"rank": 3,
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"title": "Unsupervised Learning of Neural Networks to Explain Neural Networks",
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"rank": 4,
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"title": "Explaining How a Neural Network Play the Go Game and Let People Learn",
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"rank": 5,
"arxiv_id": "2307.02129",
"title": "How Deep Neural Networks Learn Compositional Data: The Random Hierarchy Model",
"category": "?"
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{
"rank": 6,
"arxiv_id": "2303.14937",
"title": "LEURN: Learning Explainable Univariate Rules with Neural Networks",
"category": "?"
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{
"rank": 7,
"arxiv_id": "1902.08160",
"title": "Topology of Learning in Artificial Neural Networks",
"category": "?"
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{
"rank": 8,
"arxiv_id": "1502.04042",
"title": "Abstract Learning via Demodulation in a Deep Neural Network",
"category": "?"
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{
"rank": 9,
"arxiv_id": "1911.05640",
"title": "Neural Network Processing Neural Networks: An efficient way to learn higher order functions",
"category": "?"
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"rank": 10,
"arxiv_id": "2206.01717",
"title": "A Theoretical Analysis on Feature Learning in Neural Networks: Emergence from Inputs and Advantage over Fixed Features",
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"band": "F",
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"rank": 1,
"arxiv_id": "2302.09378",
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"category": "?"
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"rank": 2,
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"title": "Understanding and contextualising diffusion models",
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"rank": 3,
"arxiv_id": "2305.19066",
"title": "Nested Diffusion Processes for Anytime Image Generation",
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"rank": 4,
"arxiv_id": "2209.10948",
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{
"rank": 5,
"arxiv_id": "2411.07449",
"title": "Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution",
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"rank": 6,
"arxiv_id": "2303.05275",
"title": "Detecting Images Generated by Diffusers",
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{
"rank": 7,
"arxiv_id": "2212.00235",
"title": "VIDM: Video Implicit Diffusion Models",
"category": "?"
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{
"rank": 8,
"arxiv_id": "2301.13188",
"title": "Extracting Training Data from Diffusion Models",
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"rank": 9,
"arxiv_id": "2306.08103",
"title": "Generating Images with 3D Annotations Using Diffusion Models",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2306.05182",
"title": "Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models",
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"band": "F",
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"rank": 1,
"arxiv_id": "2305.03195",
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"rank": 2,
"arxiv_id": "2303.08774",
"title": "GPT-4 Technical Report",
"category": "?"
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{
"rank": 3,
"arxiv_id": "2304.10592",
"title": "MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models",
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"rank": 4,
"arxiv_id": "2411.17976",
"title": "The importance of visual modelling languages in generative software engineering",
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{
"rank": 5,
"arxiv_id": "2503.23601",
"title": "Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework",
"category": "?"
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"rank": 6,
"arxiv_id": "2409.15981",
"title": "GPT-4 as a Homework Tutor can Improve Student Engagement and Learning Outcomes",
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{
"rank": 7,
"arxiv_id": "2402.17396",
"title": "Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies",
"category": "?"
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"rank": 8,
"arxiv_id": "2310.11458",
"title": "GPT-4 as an interface between researchers and computational software: improving usability and reproducibility",
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{
"rank": 9,
"arxiv_id": "2305.04160",
"title": "X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages",
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"rank": 10,
"arxiv_id": "2311.15732",
"title": "GPT4Vis: What Can GPT-4 Do for Zero-shot Visual Recognition?",
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"band": "G",
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"rank": 1,
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"title": "Self-supervised Contrastive Learning for Cross-domain Hyperspectral Image Representation",
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"rank": 2,
"arxiv_id": "2301.12541",
"title": "Supervised and Contrastive Self-Supervised In-Domain Representation Learning for Dense Prediction Problems in Remote Sensing",
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"rank": 3,
"arxiv_id": "2210.03163",
"title": "Brief Introduction to Contrastive Learning Pretext Tasks for Visual Representation",
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{
"rank": 4,
"arxiv_id": "2204.13386",
"title": "Self-supervised Contrastive Learning for Audio-Visual Action Recognition",
"category": "?"
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{
"rank": 5,
"arxiv_id": "2208.04278",
"title": "Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation",
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{
"rank": 6,
"arxiv_id": "2401.12024",
"title": "Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training",
"category": "?"
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{
"rank": 7,
"arxiv_id": "2112.05760",
"title": "Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications",
"category": "?"
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{
"rank": 8,
"arxiv_id": "2103.13517",
"title": "A Broad Study on the Transferability of Visual Representations with Contrastive Learning",
"category": "?"
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{
"rank": 9,
"arxiv_id": "2106.15788",
"title": "Exploring Localization for Self-supervised Fine-grained Contrastive Learning",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2105.05682",
"title": "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning",
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{
"band": "G",
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"rank": 1,
"arxiv_id": "2306.08543",
"title": "MiniLLM: Knowledge Distillation of Large Language Models",
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"rank": 2,
"arxiv_id": "2504.13825",
"title": "Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models",
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{
"rank": 3,
"arxiv_id": "2504.14366",
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"category": "?"
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{
"rank": 4,
"arxiv_id": "2412.19449",
"title": "Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models",
"category": "?"
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{
"rank": 5,
"arxiv_id": "2403.06414",
"title": "Evolving Knowledge Distillation with Large Language Models and Active Learning",
"category": "?"
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{
"rank": 6,
"arxiv_id": "2406.17328",
"title": "Dual-Space Knowledge Distillation for Large Language Models",
"category": "?"
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{
"rank": 7,
"arxiv_id": "2002.00733",
"title": "Generation-Distillation for Efficient Natural Language Understanding in Low-Data Settings",
"category": "?"
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{
"rank": 8,
"arxiv_id": "2402.04616",
"title": "Beyond Answers: Transferring Reasoning Capabilities to Smaller LLMs Using Multi-Teacher Knowledge Distillation",
"category": "?"
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{
"rank": 9,
"arxiv_id": "2505.15442",
"title": "On the Generalization vs Fidelity Paradox in Knowledge Distillation",
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{
"rank": 10,
"arxiv_id": "2409.12500",
"title": "LLMR: Knowledge Distillation with a Large Language Model-Induced Reward",
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"band": "G",
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"rank": 1,
"arxiv_id": "2208.04852",
"title": "Graph neural networks for the prediction of molecular structure-property relationships",
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"rank": 2,
"arxiv_id": "1909.00259",
"title": "Gated Graph Recursive Neural Networks for Molecular Property Prediction",
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{
"rank": 3,
"arxiv_id": "2008.12187",
"title": "Graph Neural Network Architecture Search for Molecular Property Prediction",
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{
"rank": 4,
"arxiv_id": "2005.13607",
"title": "Multi-View Graph Neural Networks for Molecular Property Prediction",
"category": "?"
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{
"rank": 5,
"arxiv_id": "2410.12156",
"title": "FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability",
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"rank": 6,
"arxiv_id": "2410.11323",
"title": "KA-GNN: Kolmogorov-Arnold Graph Neural Networks for Molecular Property Prediction",
"category": "?"
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"rank": 7,
"arxiv_id": "2104.06046",
"title": "Which Hyperparameters to Optimise? An Investigation of Evolutionary Hyperparameter Optimisation in Graph Neural Network For Molecular Property Prediction",
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"rank": 8,
"arxiv_id": "2106.15529",
"title": "On Graph Neural Network Ensembles for Large-Scale Molecular Property Prediction",
"category": "?"
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{
"rank": 9,
"arxiv_id": "2012.01981",
"title": "Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2405.05205",
"title": "Hybrid Quantum Graph Neural Network for Molecular Property Prediction",
"category": "?"
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"band": "G",
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"rank": 1,
"arxiv_id": "2402.09748",
"title": "Model Compression and Efficient Inference for Large Language Models: A Survey",
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"rank": 2,
"arxiv_id": "2410.17170",
"title": "Self-calibration for Language Model Quantization and Pruning",
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"rank": 3,
"arxiv_id": "2412.11629",
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"rank": 4,
"arxiv_id": "2412.11494",
"title": "FTP: A Fine-grained Token-wise Pruner for Large Language Models via Token Routing",
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"rank": 5,
"arxiv_id": "2402.02834",
"title": "Shortened LLaMA: Depth Pruning for Large Language Models with Comparison of Retraining Methods",
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{
"rank": 6,
"arxiv_id": "2309.09507",
"title": "Pruning Large Language Models via Accuracy Predictor",
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{
"rank": 7,
"arxiv_id": "2102.11289",
"title": "Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference",
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"rank": 8,
"arxiv_id": "2502.15796",
"title": "Pruning as a Defense: Reducing Memorization in Large Language Models",
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{
"rank": 9,
"arxiv_id": "2502.04348",
"title": "Prompt-based Depth Pruning of Large Language Models",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2503.15369",
"title": "EfficientLLaVA:Generalizable Auto-Pruning for Large Vision-language Models",
"category": "?"
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],
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{
"band": "G",
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"latency_ms": 967.5587000092492,
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"rank": 1,
"arxiv_id": "2202.12813",
"title": "Causal discovery for observational sciences using supervised machine learning",
"category": "?"
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"rank": 2,
"arxiv_id": "2002.02770",
"title": "A Survey on Causal Inference",
"category": "?"
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{
"rank": 3,
"arxiv_id": "2011.12508",
"title": "Causal inference using deep neural networks",
"category": "?"
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{
"rank": 4,
"arxiv_id": "2101.00878",
"title": "The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies",
"category": "?"
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{
"rank": 5,
"arxiv_id": "2009.10982",
"title": "An Introduction to Proximal Causal Learning",
"category": "?"
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{
"rank": 6,
"arxiv_id": "2012.06865",
"title": "From controlled to undisciplined data: estimating causal effects in the era of data science using a potential outcome framework",
"category": "?"
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{
"rank": 7,
"arxiv_id": "1805.06826",
"title": "The Blessings of Multiple Causes",
"category": "?"
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{
"rank": 8,
"arxiv_id": "1910.09648",
"title": "Causal bootstrapping",
"category": "?"
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{
"rank": 9,
"arxiv_id": "2009.13472",
"title": "Targeted VAE: Variational and Targeted Learning for Causal Inference",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2406.02584",
"title": "A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty",
"category": "?"
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"band": "G",
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"rank": 1,
"arxiv_id": "1603.02185",
"title": "Distributed Multi-Task Learning with Shared Representation",
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{
"rank": 2,
"arxiv_id": "1603.02041",
"title": "Learning Shared Representations in Multi-task Reinforcement Learning",
"category": "?"
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{
"rank": 3,
"arxiv_id": "2102.06177",
"title": "Multi-Task Reinforcement Learning with Context-based Representations",
"category": "?"
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{
"rank": 4,
"arxiv_id": "2206.12441",
"title": "Joint Representation Training in Sequential Tasks with Shared Structure",
"category": "?"
},
{
"rank": 5,
"arxiv_id": "2202.10066",
"title": "Multi-task Representation Learning with Stochastic Linear Bandits",
"category": "?"
},
{
"rank": 6,
"arxiv_id": "2409.16651",
"title": "Learning Representation for Multitask learning through Self Supervised Auxiliary learning",
"category": "?"
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{
"rank": 7,
"arxiv_id": "1804.08139",
"title": "Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks",
"category": "?"
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{
"rank": 8,
"arxiv_id": "2304.01354",
"title": "Functional Knowledge Transfer with Self-supervised Representation Learning",
"category": "?"
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{
"rank": 9,
"arxiv_id": "1812.00500",
"title": "Multi-task Learning of Hierarchical Vision-Language Representation",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2309.16921",
"title": "YOLOR-Based Multi-Task Learning",
"category": "?"
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],
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{
"band": "H",
"query": "how to fine-tune a pre-trained language model for classification",
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"latency_ms": 1168.5913000255823,
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"rank": 1,
"arxiv_id": "2004.11493",
"title": "UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection",
"category": "?"
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{
"rank": 2,
"arxiv_id": "2305.19249",
"title": "Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models",
"category": "?"
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{
"rank": 3,
"arxiv_id": "2305.00350",
"title": "POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models",
"category": "?"
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{
"rank": 4,
"arxiv_id": "2305.18324",
"title": "Regex-augmented Domain Transfer Topic Classification based on a Pre-trained Language Model: An application in Financial Domain",
"category": "?"
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{
"rank": 5,
"arxiv_id": "1907.05338",
"title": "To Tune or Not To Tune? How About the Best of Both Worlds?",
"category": "?"
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{
"rank": 6,
"arxiv_id": "1910.11959",
"title": "FineText: Text Classification via Attention-based Language Model Fine-tuning",
"category": "?"
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{
"rank": 7,
"arxiv_id": "2004.03829",
"title": "Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning",
"category": "?"
},
{
"rank": 8,
"arxiv_id": "2004.14218",
"title": "Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning",
"category": "?"
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{
"rank": 9,
"arxiv_id": "2105.12192",
"title": "NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains",
"category": "?"
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{
"rank": 10,
"arxiv_id": "2210.16771",
"title": "Parameter-Efficient Tuning Makes a Good Classification Head",
"category": "?"
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],
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{
"band": "H",
"query": "implementing attention mechanism from scratch",
"description": "Implementation-level detail \u2014 attention tutorials, scaled dot product",
"rewrite": "Self-Attention Mechanism Implementation Transformer Architecture",
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"rank": 1,
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