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N+1 ways to implement attention
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conferences
2023 NeurIPS
2024 NeurIPS
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Mistral7B
papers
2023-04-14 - Combined Scaling for Zero-shot Transfer Learning
2023-12-04 - MobileCLIP - Fast Image-Text Models through Multi-Modal Reinforced Training
2023-12-04 - Rejuvenating image-GPT as Strong Visual Representation Learners
2023-12-05 - Mamba Linear-Time Sequence Modeling with Selective State Spaces
2023-12-09 - SILC Improving Vision Language Pretraining with Self-Distillation
2023-12-09 - Text as Image Learning Transferable Adapter for Multi-Label Classification
2023-12-17 - Stable and low-precision training for large-scale vision-language models
2024-10-04 - Movie Gen A Cast of Media Foundation Models
2024-10-10 - Pixtral 12B
2024-11-03 - GATED DELTA NETWORKS IMPROVING MAMBA2 WITH DELTA RULE
2024-11-03 - On the Efficiency of Convolutional Neural Networks
2024-11-03 - ReMoE FULLY DIFFERENTIABLE MIXTURE-OF-EXPERTS WITH RELU ROUTING
2024-11-03 - TokenFormer - RETHINKING TRANSFORMER SCAL-ING WITH TOKENIZED MODEL PARAMETERS
2024-11-17 - Mixture-of-Transformers A Sparse and Scalable Architecture for Multi-Modal Foundation Models
projects
AI Web Browser
research ideas
Bad apples for label noise early stopping
Early Fusion Multimodal Encoder Models
Latent Transformers with small vocabularies
Learn to Initialize from OS Models
Learning Skip Layers
Mixture of Modules
Multi Modal Learning to Rank as a replacement for CLIP
Neural Architecture Search for SSM Hybrids
Predict token from positional embedding
Pretrain on synthetic conversation data
Recurrent Computation with Transformers by repeating layers
Remove all the things
Sapiens for Robotics
Small Proxy model to predict loss for given sample
SSMs 4 Rec
Task Routing for Multimodal LLMs
Teach VLM to Zoom and Pan
Tiny Foundational model by distilling from a lot of SOTA models
Tiny LLMs with rag in the middle
Two Stream SSMs
Universal embedding space for popular foundational models (or adapters)
Untitled
VLMs for better Vision Backbones
White space separated conv text encoder
"World Models" - Modeling the Real World
3D Computer Vision
A glossary of all the ways ML models fail to train
Activation Functions
Active Learning
Agents
Alignment and Post Training
Approximate Nearest Neighbor Search (ANN)
autograd
Autonomous Driving - Self Driving
benchmarks
CLIP
Cloud GPUs
cnns
Code LLMs
compilers
compression
Computer Graphics
Computer Vision Backbones
Contrastive Learning
Data Curation
Data Formats for ML
Data Loading
Decoder Transformer Inference
Decoding and Sampling
Deep Learning Tricks of the Trade
Deepspeed
Diffusion Models
Distributed Training
Document Processing
Embedding Models
Evaluation Metrics
Extreme Classification
FairScale
feature-stores
Few Shot Learning
fine-tuning
Flow Matching - Rectified Flows
Food Recognition
Function Calling (with LLMs)
Generative Models
GPUs
graphs
Hallucinations
Human Pose Estimation and Human Modeling
Image Matching
Image Recognition
Imitation Learning
Instance Recognition and Retrieval
Instance Retrieval and Instance Recognition
jax
Label Noise
Learning to Rank
LLM Training and Tuning
logsumexp
Long Context Transformers
Long Tail Classification and Class Imbalance
Machine Learning Tricks and Best Practices
maes
Mamba
matryoshka embeddings
Mechanistic Interpretability
medical
mixture of experts
ML Competitions
ML Conferences
ML Courses & Books
ML for Math
ML Infrastructure
ML Scaling
MLX
Mobile Inference
Model Distillation and Transfer Learning
Model Routing
Multi Label Classification
multi-modal
multi-task
Natural Language Processing
nerf
Networking
Neural Architecture Search (NAS)
Normalization
Numerics
Object Detection
ocr
paper-params
Parameter Efficient Fine Tuning (PEFT)
PrefixLM
Pruning
Quantization
Recommendation Systems
Reinforcement Learning (RL)
resources
Retrieval Augmented Generation (RAG)
Retrieval Augmented Models
RL for LMs
Robotics
segmentation
Self-Supervised Image Models
Semantic Search and Ranking
Semi Supervised Learning
Server Inference
SLAM
Small Foundational Models
softmax
speech
Speedruns
State Space Models (SSMs)
Storage
Structured Generation with LLMs
Synthetic Data
Tabular Machine Learning
Tensor Tricks
Test Time Compute and LLM Reasoning
Text Embeddings
text2sql
Token Dropping, Pruning, Merging and Compression
torch compile
Transformer Alternatives (mostly SSMs)
Transformer Properties
transformers
triton
Untitled
Variational Autoencoders (VAE)
video
Video Generation
Vision Language Models
Vision Transformers
Visual Search
xformers
xlstm
wiki
C4AI Command R7B
Conformer
Contextual Document Embeddings (CDE)
ControlNet
DeltaNet
DETR
Diffusion Transformer (DiT)
FLUX
Gecko - Versatile Text Embeddings Distilled from Large Language Models
HNSW
InternVL
Kolmogorov-Arnold Theorem
KV Cache Compression
Latent Diffusion
LayerSkip
LO-PQ
Maximal Update Parametrization (μP)
Mixture of Depth
Mixture-of-Transformer
MMDiT - Multi Modal Diffusion Transformer
ModernBERT
Movie Gen
Multi-Head Latent Attention (MLA)
Not All Tokens Are What You Need For Pretraining
PaliGemma
Speech-to-Speech
Stable Diffusion 3 and 3.5
Test Time Learning (Local Learning)
Token Dropping
Tokenization
Unified-IO
Vision-Language-Action Models (VLA)
Wav2vec
WaveNet
You Only Cache Once (YOCO)
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Explorer
blog
2022-12-17 TIL
N+1 ways to implement attention
journal
2024-08-12
2024-08-19
2024-08-26
2024-09-02
2024-09-09
2024-09-16
2024-09-18 - Pytorch Conference Notes
2024-09-23
2024-09-30
2024-10-07
2024-10-14
2024-10-21
2024-10-28
2024-11-04
2024-11-18
2024-11-25
2024-12-02
2024-12-09
2024-12-16
2024-12-23
2024-12-30
2025-01-06
notes
business
accounting
consulting
Growth
Landing Pages
legal
marketing
Open Source Business Models
pricing
Productivity Software
sales
VC Alternatives
dev
Algorithms
Quickselect
Resources
Static Search Trees
algorithms
arrow
bashrc x zshrc
ClickHouse
cloud
Concurrency
CRDTs
cuda
data visualization and dashboarding
Databases
django
docker
duckdb
Engineering Blogs
ffmpeg
hardware
Kafka
kubernetes
Latencies
Leetcode
logging
networking
object-stores
parquet
postgres
python
pytorch
ray
react-native
redis
rust
search
security
sqlite
terraform
Untitled
web-servers
math
Linear Algebra
Math for ML
Optimization
Probability
ml
conferences
2023 NeurIPS
2024 NeurIPS
models
Mistral7B
papers
2023-04-14 - Combined Scaling for Zero-shot Transfer Learning
2023-12-04 - MobileCLIP - Fast Image-Text Models through Multi-Modal Reinforced Training
2023-12-04 - Rejuvenating image-GPT as Strong Visual Representation Learners
2023-12-05 - Mamba Linear-Time Sequence Modeling with Selective State Spaces
2023-12-09 - SILC Improving Vision Language Pretraining with Self-Distillation
2023-12-09 - Text as Image Learning Transferable Adapter for Multi-Label Classification
2023-12-17 - Stable and low-precision training for large-scale vision-language models
2024-10-04 - Movie Gen A Cast of Media Foundation Models
2024-10-10 - Pixtral 12B
2024-11-03 - GATED DELTA NETWORKS IMPROVING MAMBA2 WITH DELTA RULE
2024-11-03 - On the Efficiency of Convolutional Neural Networks
2024-11-03 - ReMoE FULLY DIFFERENTIABLE MIXTURE-OF-EXPERTS WITH RELU ROUTING
2024-11-03 - TokenFormer - RETHINKING TRANSFORMER SCAL-ING WITH TOKENIZED MODEL PARAMETERS
2024-11-17 - Mixture-of-Transformers A Sparse and Scalable Architecture for Multi-Modal Foundation Models
projects
AI Web Browser
research ideas
Bad apples for label noise early stopping
Early Fusion Multimodal Encoder Models
Latent Transformers with small vocabularies
Learn to Initialize from OS Models
Learning Skip Layers
Mixture of Modules
Multi Modal Learning to Rank as a replacement for CLIP
Neural Architecture Search for SSM Hybrids
Predict token from positional embedding
Pretrain on synthetic conversation data
Recurrent Computation with Transformers by repeating layers
Remove all the things
Sapiens for Robotics
Small Proxy model to predict loss for given sample
SSMs 4 Rec
Task Routing for Multimodal LLMs
Teach VLM to Zoom and Pan
Tiny Foundational model by distilling from a lot of SOTA models
Tiny LLMs with rag in the middle
Two Stream SSMs
Universal embedding space for popular foundational models (or adapters)
Untitled
VLMs for better Vision Backbones
White space separated conv text encoder
"World Models" - Modeling the Real World
3D Computer Vision
A glossary of all the ways ML models fail to train
Activation Functions
Active Learning
Agents
Alignment and Post Training
Approximate Nearest Neighbor Search (ANN)
autograd
Autonomous Driving - Self Driving
benchmarks
CLIP
Cloud GPUs
cnns
Code LLMs
compilers
compression
Computer Graphics
Computer Vision Backbones
Contrastive Learning
Data Curation
Data Formats for ML
Data Loading
Decoder Transformer Inference
Decoding and Sampling
Deep Learning Tricks of the Trade
Deepspeed
Diffusion Models
Distributed Training
Document Processing
Embedding Models
Evaluation Metrics
Extreme Classification
FairScale
feature-stores
Few Shot Learning
fine-tuning
Flow Matching - Rectified Flows
Food Recognition
Function Calling (with LLMs)
Generative Models
GPUs
graphs
Hallucinations
Human Pose Estimation and Human Modeling
Image Matching
Image Recognition
Imitation Learning
Instance Recognition and Retrieval
Instance Retrieval and Instance Recognition
jax
Label Noise
Learning to Rank
LLM Training and Tuning
logsumexp
Long Context Transformers
Long Tail Classification and Class Imbalance
Machine Learning Tricks and Best Practices
maes
Mamba
matryoshka embeddings
Mechanistic Interpretability
medical
mixture of experts
ML Competitions
ML Conferences
ML Courses & Books
ML for Math
ML Infrastructure
ML Scaling
MLX
Mobile Inference
Model Distillation and Transfer Learning
Model Routing
Multi Label Classification
multi-modal
multi-task
Natural Language Processing
nerf
Networking
Neural Architecture Search (NAS)
Normalization
Numerics
Object Detection
ocr
paper-params
Parameter Efficient Fine Tuning (PEFT)
PrefixLM
Pruning
Quantization
Recommendation Systems
Reinforcement Learning (RL)
resources
Retrieval Augmented Generation (RAG)
Retrieval Augmented Models
RL for LMs
Robotics
segmentation
Self-Supervised Image Models
Semantic Search and Ranking
Semi Supervised Learning
Server Inference
SLAM
Small Foundational Models
softmax
speech
Speedruns
State Space Models (SSMs)
Storage
Structured Generation with LLMs
Synthetic Data
Tabular Machine Learning
Tensor Tricks
Test Time Compute and LLM Reasoning
Text Embeddings
text2sql
Token Dropping, Pruning, Merging and Compression
torch compile
Transformer Alternatives (mostly SSMs)
Transformer Properties
transformers
triton
Untitled
Variational Autoencoders (VAE)
video
Video Generation
Vision Language Models
Vision Transformers
Visual Search
xformers
xlstm
wiki
C4AI Command R7B
Conformer
Contextual Document Embeddings (CDE)
ControlNet
DeltaNet
DETR
Diffusion Transformer (DiT)
FLUX
Gecko - Versatile Text Embeddings Distilled from Large Language Models
HNSW
InternVL
Kolmogorov-Arnold Theorem
KV Cache Compression
Latent Diffusion
LayerSkip
LO-PQ
Maximal Update Parametrization (μP)
Mixture of Depth
Mixture-of-Transformer
MMDiT - Multi Modal Diffusion Transformer
ModernBERT
Movie Gen
Multi-Head Latent Attention (MLA)
Not All Tokens Are What You Need For Pretraining
PaliGemma
Speech-to-Speech
Stable Diffusion 3 and 3.5
Test Time Learning (Local Learning)
Token Dropping
Tokenization
Unified-IO
Vision-Language-Action Models (VLA)
Wav2vec
WaveNet
You Only Cache Once (YOCO)
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