Books:

1. Introduction to Statistical Learning by Trevor Hastie

• https://www.statlearning.com

2. Elements of Statistical Learning by Trevor Hastie

• https://hastie.su.domains/Papers/ESLII.pdf

3. Dive Deep into Deep Learning by Aston Zhang

• https://d2l.ai

4. Forecasting: Principles and Practice by Hyndman

• https://otexts.com/fpp3/

5. Reinforcement Learning: An Introduction by Sutton

6. Data Engineering with AWS by Gareth Eagar

7. Recommender Systems by Charu C. Aggarwal

8. Anomaly Detection Principles and Algorithms by Kishan G. Mehrotra

Papers:

Computer Vision

Architectures:

Understanding Convolutional Networks with multi-layered Deconvolutional Network:

• https://arxiv.org/pdf/1311.2901.pdf

Texture synthesis using Convolutional Neural Networks:

• https://arxiv.org/pdf/1505.07376.pdf

JEPA pre-trained video embedding for vision tasks:

• https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/

DETR (Object detection with vision transformers):

• https://arxiv.org/abs/2005.12872

Deep Supervision Distillation:

• https://arxiv.org/pdf/2202.07846.pdf

Deformable convolutions:

• https://arxiv.org/pdf/1703.06211.pdf

DeformableDETR (Deformable Attention):

• https://arxiv.org/pdf/2010.04159.pdf

SwinTransformer (Shifted Window Attention):

• https://arxiv.org/pdf/2103.14030.pdf

Dino-DETR:

• https://arxiv.org/pdf/2203.03605.pdf

Anchor-Queries (DAB-DETR):

• https://arxiv.org/pdf/2201.12329.pdf

Object detection on compressed video:

• https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Fast_Object_Detection_in_Compressed_Video_ICCV_2019_paper.pdf

Classifying JPEG compressed images (directly on DCT):

• https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9145442/

Training:

Original label-smoothing paper:

• https://arxiv.org/pdf/1512.00567.pdf

Online Label-smoothing:

• https://ieeexplore.ieee.org/document/9464693

Hungarian-matching loss for single-shot detection:

• https://arxiv.org/pdf/1506.04878.pdf

Focal Loss:

• https://arxiv.org/pdf/1708.02002.pdf

GIoU:

• https://arxiv.org/pdf/1902.09630.pdf

Original Knowledge Distillation paper:

• https://arxiv.org/pdf/1503.02531.pdf

Original Deep Supervision paper:

• https://arxiv.org/pdf/1409.5185.pdf

L2 Feature Distillation:

• https://arxiv.org/pdf/1412.6550

Theoretical underpinnings of Knowledge Distillation:

• https://arxiv.org/pdf/2002.03532.pdf

Feature Distillation + Knowledge Distillation + Deep Supervision:

• https://arxiv.org/pdf/2202.07846.pdf

NLP

Attention is All you need:

• https://arxiv.org/abs/1706.03762

GLoVe embedding:

• https://aclanthology.org/D14-1162.pdf

FastText embedding:

• https://arxiv.org/abs/1607.04606

LLM’s escalation risks in political situations:

• https://arxiv.org/abs/2401.03408

GPT-1:

• https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf

BERT:

• https://arxiv.org/pdf/1810.04805.pdf

Multimodal Learning:

Show attend and tell:

• https://arxiv.org/pdf/1502.03044

CLIP:

• https://arxiv.org/abs/2103.00020

Image-to-text pretraining for one-shot object detection tasks:

• https://arxiv.org/abs/2205.06230

GenAI:

Auto-encoding Variational Bayes:

• https://arxiv.org/pdf/1312.6114.pdf

GAN:

• https://arxiv.org/pdf/1406.2661.pdf

DeepConvolutionalGAN:

• https://arxiv.org/pdf/1511.06434.pdf

Style Transfer:

• https://arxiv.org/pdf/1508.06576.pdf

Conditional GAN:

• https://arxiv.org/pdf/1411.1784.pdf

Deep Convolutional GAN:

• https://arxiv.org/abs/1511.06434

PatchGAN and U-Net Generator:

• https://arxiv.org/pdf/1611.07004.pdf

CycleGAN:

• https://arxiv.org/abs/1703.10593

Original Diffusion Paper:

• https://arxiv.org/pdf/1503.03585

Tabular ML

XGBoost:

• https://arxiv.org/pdf/1603.02754

Why Neural Networks don't perform well on Tabular:

• https://proceedings.neurips.cc/paper_files/paper/2022/file/0378c7692da36807bdec87ab043cdadc-Paper-Datasets_and_Benchmarks.pdf