paper

1.[논문리뷰 및 구현] Deep Residual Learning for Image Recognition

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2.[논문 리뷰 및 구현]Identity Mappings in Deep Residual Networks

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3.[논문 리뷰 및 구현]Aggregated Residual Transformations for Deep Neural Networks

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4.[논문 리뷰 및 구현] Attention is all you need

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5.[논문 리뷰 및 구현] AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

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6.[논문 리뷰 및 구현] A ConvNet for the 2020s

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7.[논문 리뷰] TabTransformer: Tabular Data Modeling Using Contextual Embeddings

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8.[논문 리뷰 및 구현] MLP-Mixer: An all-MLP Architecture for Vision

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10.[논문리뷰 및 구현] Patches Are All You Need?

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11.[논문리뷰] Unsupervised Visual Representation Learning by Context Prediction

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12.[논문리뷰] TransTab: Learning Transferable Tabular Transformers Across Tables

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13.[논문리뷰]Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles

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14.[논문 리뷰] Colorization as a proxy task for visual understanding

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15.[논문 리뷰 및 구현] UNSUPERVISED REPRESENTATION LEARNING BY PREDICTING IMAGE ROTATIONS

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16.[논문 리뷰] A Simple Framework for Contrastive Learning of Visual Representations

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17.[논문 리뷰 및 구현] Momentum Contrast for Unsupervised Visual Representation Learning

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18.[논문 리뷰 및 구현] Improved Baselines with Momentum Contrastive Learning

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19.[논문 리뷰] Representation Learning with Contrastive Predictive Coding

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20.[논문 리뷰] Contrastive Multiview Coding

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21.[논문 리뷰] What Makes for Good Views for Contrastive Learning?

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22.[논문 리뷰 및 구현] Bootstrap Your Own Latent A New Approach to Self-Supervised Learning

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23.[논문 리뷰 및 구현]Exploring Simple Siamese Representation Learning

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24.[논문 리뷰및 구현] Playing Atari with Deep Reinforcement Learning

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25.[논문 리뷰]i-MIX: A DOMAIN-AGNOSTIC STRATEGY FOR CONTRASTIVE REPRESENTATION LEARNING - ICLR 2021

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26.[논문 리뷰] Masked Autoencoders Are Scalable Vision Learners

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27.[논문 리뷰] SELF-SUPERVISED REPRESENTATION LEARNING FROM RANDOM DATA PROJECTORS - ICLR 2024

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28.[논문 리뷰]Asynchronous Methods for Deep Reinforcement Learning

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30. [논문 리뷰] World Models

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32.[논문 리뷰]BEiT: BERT Pre-Training of Image Transformers

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33.[논문 리뷰] IBOT : IMAGE BERT PRE-TRAINING WITH ONLINE TOKENIZER

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34.[논문 리뷰]SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning

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35.[논문 리뷰 및 구현]Learning Latent Dynamics for Planning from Pixels

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37.[논문 리뷰 및 구현] MASTERING ATARI WITH DISCRETE WORLD MODELS

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38.[논문 리뷰] Mastering Diverse Domains through World Models

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39.[논문 리뷰] DIFFERENTIAL TRANSFORMER

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40.[논문 리뷰] TransDreamer: Reinforcement Learning with Transformer World Models

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41.[논문 리뷰] TabNet: Attentive Interpretable Tabular Learning

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42.[논문 리뷰]Discovering and Achieving Goals via World Models

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43.[논문 리뷰]PERCEIVER IO: A GENERAL ARCHITECTURE FOR STRUCTURED INPUTS & OUTPUTS

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44.[논문 리뷰 및 구현]Denoising Diffusion Probabilistic Models

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45.[논문 리뷰]Trust Region Policy Optimization

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46.[논문 리뷰]UNITABE: A UNIVERSAL PRETRAINING PROTOCOL FOR TABULAR FOUNDATION MODEL IN DATA SCIENCE - ICLR 2024

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47.[논문 리뷰]XTab: Cross-table Pretraining for Tabular Transformers - ICML 2023

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48.[논문 리뷰]Proximal Policy Optimization Algorithms

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50.[논문 리뷰]Mastering the Game of Go without Human Knowledge

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51.[논문 리뷰]Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

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52.[논문 리뷰]Planning with Diffusion for Flexible Behavior Synthesis

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53.[논문 리뷰]Neural Discrete Representation Learning

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54.[논문 리뷰]DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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55.[논문 리뷰]Probabilistic Inference in Language Models via Twisted Sequential Monte Carlo

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56.[논문 리뷰]Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach

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57.[논문 리뷰 및 구현]CONDITIONAL POSITIONAL ENCODINGS FOR VISION TRANSFORMERS

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58.[논문 리뷰] Monte Carlo Tree Diffusion for System 2 Planning

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59.[논문 리뷰]Offline Reinforcement Learning with Discrete Diffusion Skills

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60.[논문 리뷰]d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning

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61.[논문 리뷰] Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking

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62.[논문 리뷰] Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation

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63.[논문 리뷰] Thinking Augmented Pre-training

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