아카이브
- 26 / 03 [Tech Review] 당근마켓 ML
- 21 / 03 Summary
- 21 / 03 Summary
- 10 / 03 5-1. Unconstrained Optimization(Line search)
- 06 / 02 [Paper Review] Deep Neural Networks for YouTube Recommendations
- 19 / 01 [Paper Review] SSD: Single Shot MultiBox Detector
- 17 / 01 [Paper Review] You Only Look Once: Unified, Real-Time Object Detection
- 15 / 01 [Paper Review] Very Deep Convolutional Networks for Large-Scale Image Recognition
- 15 / 01 [Paper Review] Rich feature hierarchies for accurate object detection and semantic segmentation
- 07 / 01 [Paper Review] Aggregated Residual Transformations for Deep Neural Networks
- 05 / 01 [Paper Review] Deep Residual Learning for Image Recognition
- 30 / 12 [Paper Review] Dropout Reduces Underfitting
- 20 / 12 [Paper Review] DeiT III: Revenge of the ViT
- 20 / 12 [Paper Review] Training data-efficient image transformers & distillation through attention
- 18 / 12 TODO list
- 18 / 12 [Paper Review] Vision Transformers Need Registers
- 17 / 12 [Paper Review] DINOv2: Learning Robust Visual Features without Supervision
- 16 / 12 [Paper Review] iBOT: Image BERT Pre-Training with Online Tokenizer
- 15 / 12 [Paper Review] BEiT: BERT Pre-Training of Image Transformers
- 13 / 12 [Paper Review] Emerging Properties in Self-Supervised Vision Transformers
- 06 / 12 [Paper Review] Vision Transformer Adapter for Dense Predictions
- 28 / 11 [Paper Review] Bootstrap your own latent: A new approach to self-supervised Learning
- 21 / 11 [Paper Review] MLP-Mixer: An all-MLP Architecture for Vision
- 15 / 11 [Paper Review] BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
- 01 / 11 [Paper Review] Expanding Language-Image Pretrained Models for General Video Recognition
- 01 / 11 [Paper Review] Learning Transferable Visual Models From Natural Language Supervision
- 25 / 10 [Paper Review] DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
- 23 / 10 [Paper Review] Deformable DETR: Deformable Transformers for End-to-End Object Detection
- 15 / 10 [Paper Review] Attention Bottlenecks for Multimodal Fusion
- 13 / 10 [Paper Review] Perceiver: General Perception with Iterative Attention
- 11 / 10 [Paper Review] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
- 11 / 10 [Paper Review] Masked Autoencoders Are Scalable Vision Learners
- 07 / 10 [Paper Review] End-to-End Object Detection with Transformers
- 04 / 10 [Paper Review] PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
- 30 / 07 1. Huggingface
- 10 / 06 10. Self-Supervised Learning
- 05 / 06 9. Natural Language Processing
- 28 / 05 4. 3D Computer Vision
- 20 / 05 3. Generative Model
- 16 / 05 5. Algorithm
- 05 / 05 4. KKT Condition
- 02 / 05 2. Visual Correspondence(2)
- 02 / 05 1. Visual Correspondence(1)
- 01 / 05 7. Machine Learning
- 08 / 04 4. Control
- 07 / 04 3. Planning
- 06 / 04 3. Convex Optimization
- 05 / 04 6. First-Order Logic
- 04 / 04 5. Propositional Logic
- 03 / 04 4. CSP
- 02 / 04 3. Adversarial Search
- 01 / 04 2. Search
- 31 / 03 1. Introduction
- 30 / 03 8. Segmentation(Instance, Panoptic)
- 29 / 03 7. Segmentation(Semantic)
- 28 / 03 6. Object Detection(One-Stage)
- 27 / 03 5. Object Detection(Two-Stage)
- 26 / 03 3. Perception(2)
- 25 / 03 2. Perception(1)
- 19 / 03 4. Attention
- 18 / 03 3. RNN
- 17 / 03 3. Eigenvalues
- 16 / 03 2. Convex Function
- 15 / 03 1. Convex Set
- 14 / 03 2. CNN
- 14 / 03 1. Notations
- 13 / 03 1. Feedfoward Neural Network
- 12 / 12 12. Big Data
- 11 / 12 6. Driver
- 11 / 12 11. Transaction
- 10 / 12 10. Query Processing
- 09 / 12 9. Indexing(2)
- 08 / 12 8. Indexing(1)
- 07 / 12 7. Normalization
- 06 / 12 6. EE-R Modeling
- 05 / 12 5. E-R Modeling
- 04 / 12 4. SQL Basic(2)
- 03 / 12 3. SQL Basic(1)
- 02 / 12 2. Relational Algebra
- 01 / 12 1. Intro
- 05 / 11 5. Memory Management
- 04 / 11 4. File System(memory)
- 03 / 11 3. File System(disk)
- 02 / 11 2. Process
- 01 / 11 1. Interrupt
- 04 / 06 0. Summary
- 11 / 04 1. Time Complexity