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AreaTopicPaperPaper URLTutorial URL
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Deep Learning BackgroundBrain StructureBrain Anatomical Structure and Biological Function
http://www.ifmlab.org/files/tutorial/IFMLab_Tutorial_4.pdf
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Deep Learning BackgroundBrain Neural Units
Basic Neural Units of the Brain: Neurons, Synapses and Action Potential
http://www.ifmlab.org/files/tutorial/IFMLab_Tutorial_5.pdf
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Deep Learning BackgroundBrain Cognitive Function
Cognitive Functions of the Brain: Perception, Attention and Memory
http://www.ifmlab.org/files/tutorial/IFMLab_Tutorial_6.pdf
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Deep Learning BackgroundMath BasicsThe Matrix Cookbook
https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf
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Deep Learning BackgroundDL Optimization
Gradient Descent based Optimization Algorithms for Deep Learning Models Training
http://www.ifmlab.org/files/tutorial/IFMLab_Tutorial_1.pdf
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Deep Learning BackgroundMachine Learning BasicsMachine Learning Overview
http://www.ifmlab.org/files/book/broad_learning/chap2.pdf
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Deep LearningFeedforward Network
The Forward-Forward Algorithm: Some Preliminary Investigations
https://arxiv.org/pdf/2212.13345.pdf
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Text Mining & NLPLSTMLONG SHORT-TERM MEMORY
https://www.bioinf.jku.at/publications/older/2604.pdf
https://colah.github.io/posts/2015-08-Understanding-LSTMs/
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Text Mining & NLPGRU
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
https://arxiv.org/pdf/1412.3555.pdf
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Text Mining & NLPTransformerAttention Is All You Need
https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
http://jalammar.github.io/illustrated-transformer/
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Text Mining & NLPBERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://arxiv.org/pdf/1810.04805.pdf
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Text Mining & NLPBART
BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
https://arxiv.org/pdf/1910.13461.pdf
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Text Mining & NLPGPT-1Improving Language Understanding by Generative Pre-Training
https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf
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Text Mining & NLPGPT-2Language Models are Unsupervised Multitask Learners
https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf
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Text Mining & NLPGPT-3/Priming PromptLanguage Models are Few-Shot Learnershttps://arxiv.org/pdf/2005.14165.pdf
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Text Mining & NLPInstructGPT
Training language models to follow instructions with human feedback
https://arxiv.org/pdf/2203.02155.pdf
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Text Mining & NLPDiscrete Prompt
Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference
https://aclanthology.org/2021.eacl-main.20.pdf
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Text Mining & NLPContinuous PromptPrefix-Tuning: Optimizing Continuous Prompts for Generation
https://aclanthology.org/2021.acl-long.353.pdf
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Text Mining & NLPPrompt Analysis
Do Prompt-Based Models Really Understand the Meaning of their Prompts?
https://arxiv.org/pdf/2109.01247.pdf
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Text Mining & NLPRLHFDeep Reinforcement Learning from Human Preferenceshttps://arxiv.org/pdf/1706.03741.pdf
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Text Mining & NLPPPOProximal Policy Optimization Algorithmshttps://arxiv.org/abs/1707.06347
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Text Mining & NLPDPO
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
https://arxiv.org/pdf/2305.18290
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Text Mining & NLPSelf-RewardingSelf-Rewarding Language Modelshttps://arxiv.org/pdf/2401.10020
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Image & VisionCNNGradient Based Learning Applied to Document Recognition
http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
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Image & VisionAttentionRecurrent Models of Visual Attention
https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf
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Image & VisionGANGenerative Adversarial Nets
https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf
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Image & VisionResNetDeep Residual Learning for Image Recognitionhttps://arxiv.org/pdf/1512.03385.pdf
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Image & VisionCapsuleNetDynamic Routing Between Capsuleshttps://arxiv.org/pdf/1710.09829.pdf
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Image & VisionEfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://arxiv.org/pdf/1905.11946.pdf
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Image & VisionDiffusion ModelDiffusion Models Beat GANs on Image Synthesishttps://arxiv.org/pdf/2105.05233.pdf
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Image & VisionViT
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
https://arxiv.org/pdf/2010.11929.pdf
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Image & VisionSAMSegment Anythinghttps://arxiv.org/pdf/2304.02643
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Image & VisionSAM 2SAM 2: Segment Anything in Images and Videoshttps://arxiv.org/pdf/2408.00714
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Image & VisionEfficientSAM
EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything
https://arxiv.org/pdf/2312.00863.pdf
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Image & VisionDDPMDenoising Diffusion Probabilistic Modelshttps://arxiv.org/pdf/2006.11239.pdf
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Image & VisionLDMHigh-Resolution Image Synthesis with Latent Diffusion Modelshttps://arxiv.org/pdf/2112.10752.pdf
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Image & VisionDiTScalable Diffusion Models with Transformershttps://arxiv.org/pdf/2212.09748.pdf
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Image & VisionCLIP
Learning Transferable Visual Models From Natural Language Supervision
https://arxiv.org/pdf/2103.00020
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Graph Neural NetGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://arxiv.org/pdf/1609.02907.pdf
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Graph Neural NetGATGraph Attention Networkshttps://arxiv.org/pdf/1710.10903.pdf
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Graph Neural NetGraph-Bert
Graph-Bert: Only Attention is Needed for Learning Graph Representations
https://arxiv.org/pdf/2001.05140.pdf
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Graph Neural NetOversmoothing
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
https://arxiv.org/pdf/1801.07606.pdf
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