Recommended Papers

Core Machine Learning

• Lottery Ticket Hypothesis
  https://arxiv.org/abs/1803.03635

• Double Descent
  https://arxiv.org/abs/1912.02292

Model Architectures

• ResNet
  https://arxiv.org/abs/1512.03385

• Transformer
  https://arxiv.org/abs/1706.03762

• Vision Transformer
  https://arxiv.org/abs/2010.11929

(2D) Object Detection

• Faster R-CNN
  https://arxiv.org/abs/1506.01497

• SSD
  https://arxiv.org/abs/1512.02325

• YOLO
  https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Redmon_You_Only_Look_CVPR_2016_paper.pdf

(3D) Object Detection

• PointNet
  https://arxiv.org/abs/1612.00593

• PointNet++
  https://arxiv.org/abs/1706.02413

• VoxelNet
  https://arxiv.org/abs/1711.06396

• PointRCNN
  https://arxiv.org/abs/1812.04244

• PV-RCNN
  https://arxiv.org/abs/1912.13192

Unsupervised Domain Adaptation

• Domain-Adversarial Training of Neural Networks
  https://arxiv.org/pdf/1505.07818

• Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
  https://arxiv.org/abs/1906.03950

• Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
  https://arxiv.org/abs/1712.02560

• Domain Adaptive Faster R-CNN for Object Detection in the Wild
  https://arxiv.org/abs/1803.03243

Self-supervised Learning

• MoCo
  https://arxiv.org/abs/1911.05722

• SimCLR
  https://arxiv.org/abs/2002.05709

• BYOL
  https://arxiv.org/abs/2006.07733

Implicit Neural Representations

• SDF
  https://arxiv.org/abs/1901.05103

• NeRF
  https://arxiv.org/abs/2003.08934

Generative Models

• VAE
  https://arxiv.org/abs/1312.6114

• VQ-VAE
  https://arxiv.org/abs/1711.00937

• DCGAN
  https://arxiv.org/abs/1511.06434

• StyleGAN
  https://arxiv.org/abs/1812.04948

• Denoising Diffusion Probabilistic Models
  https://arxiv.org/abs/2006.11239

• GLIDE
  https://arxiv.org/abs/2112.10741

Adversarial Attack/Defense

• FGSM
  https://arxiv.org/abs/1412.6572

• PGD
  https://arxiv.org/pdf/1706.06083.pdf

• Adversarial Examples Are Not Bugs, They Are Features
  https://arxiv.org/abs/1905.02175

Vision-Language

• CLIP
  https://arxiv.org/abs/2103.00020

• BLIP
  https://arxiv.org/abs/2201.12086

• SimVLM
  https://openreview.net/forum?id=GUrhfTuf_3

• CoCa
  https://arxiv.org/abs/2205.01917

• Dall-E
  https://arxiv.org/abs/2102.12092

• Dall-E-2
  https://arxiv.org/abs/2204.06125

Natural Language

• BERT
  https://arxiv.org/abs/1810.04805

• BART
  https://arxiv.org/abs/1910.13461

• GPT
  https://arxiv.org/abs/2005.14165

Courses & Lectures

Linear Algebra

• Gilbert Strang (MIT)
  Course Link

Optimization

• Stephen Boyd (Stanford)
  Course Link

Deep Learning

• Soheil Feizi (University of Maryland, College Park)
  Course Link

• Alexander Amini (MIT)
  Course Link

Computer Vision

• CS231n (Stanford)
  Course Link

Computer Graphics

• Keenan Crane (CMU)
  Course Link

Textbooks

Deep Learning

• Ian Goodfellow, Yoshua Bengio, Aaron Courville
  Textbook Link

Pattern Recognition and Machine Learning

• Christopher Bishop
  Textbook Link