1.
Introduction
Research Methodologies
2.
Course
3.
Seminar 1
4.
Seminar 2
5.
Extras
Models
6.
GNNs
7.
ViTs
8.
General Circulation Models
9.
Structured State Spaces
Transformer Variants
10.
MaxViT
11.
HieraViT
12.
Swin Transformer
13.
Swin Transformer V2
14.
Spatio-Temporal Swin-Transformer
15.
Multiscaled stacked ViT
16.
Efficient Transformers
16.1.
Linear Transformers
16.2.
Linear Transformers as FWP
16.3.
The Devil in Linear Transformers
16.4.
Performer
16.5.
SRFormer
17.
Test time training
18.
TTT++
19.
Universal Transformers
Weather Models
20.
Rise of Data-Driven Weather Forecasting
21.
ClimaX
22.
PanguWeather
23.
MetNet
24.
FuXi
25.
FourcastNet
26.
GraphCast
27.
PrithViWxC
28.
ORBIT
29.
Climate Benchmarks
29.1.
ClimateLearn
29.2.
ClimateBench
29.3.
WeatherBench
30.
IPCC Reports
30.1.
WG1
30.2.
WG2
30.3.
WG3
30.4.
AR6
Misc and Domain Specific Models
31.
ACE
32.
ExPT
33.
Closed-form continuous-time neural networks
34.
Recurrent Fast Weight Programmers
35.
Liquid Neural Networks
36.
Liquid S4
37.
AI CCA
38.
FireCast
39.
FarSite
40.
Wildfire risk modeling
41.
XAI wildfire in the Mediterranean countries
Scaling Benchmarks
42.
Training LLMs on leadershipâclass supercomputers
43.
Scaling Laws for LLMS
44.
Compute Optimal LLMs
45.
Data Optimal LLMs
Development
46.
Parallelization
Boring AI
47.
Data Quality
48.
Trustworthy AI
49.
Provenance Documentation for AI
50.
Information Required for XAI
51.
Explainable AI Techniques
52.
Provenance
52.1.
Open Provenance Model
52.2.
PROV Notation
52.3.
PROV Constraints
52.4.
PROV-JSON
52.5.
A Graph Model for Data Provenance
53.
Provenance Supporting Hyperparameter Analysis in DNNs
54.
FAIR Guiding Principles
55.
Advances, Challenges, and Opportunities in TAI
56.
Provenance in ML
56.1.
Provenance in ML Lifecycle
56.2.
Prov supporting Hyperparameter Analysis in DNNs
56.3.
Data Provenance for Distributed ML
56.4.
Linking PROV and XAI in distributed ML
56.5.
MLFlow2PROV
56.6.
LIMA
56.7.
ProvLake
56.8.
Efficient runtime capture of multiworkflow data using prov
56.9.
PrIU
56.10.
Provenance for Responsible AI Systems
56.11.
Provenance Documentation for XAI
56.11.1.
Data Provenance Initiative
57.
RO-Crate
58.
Workflow Run ROC
59.
Collecting Managing and Analyzing Provenance Data
60.
Provenance-based IDs
Energy Efficiency
61.
Metrics
62.
CarbonLLM
63.
HVAC
64.
Computational/Energy Use of CMIP6
65.
Energy Intensity of Internet Data Transmission
66.
Green AI
67.
Green 500 Metrics
68.
Data center EE
Growing Models
69.
LSTMVis
70.
H-State and Semantic Structure
71.
Hidden Memories of RNNs
72.
Analysis of Entropy in Hidden States
Useful Stuff
73.
Notes for Talks
73.1.
A Data-Oriented Perspective
73.2.
Ai for Good talk
74.
Tropical Cyclones
75.
Model Comparison
76.
Challenges and Opportunities
77.
FM4C Improvements
78.
WandB Webinar
79.
Zero
80.
Zero++
Ideas
81.
Fire behavior
82.
ML for Climate
83.
Incremental Models
84.
Scaling Issues
Footnotes
Light
Rust
Coal
Navy
Ayu
Latte
Frappé
Macchiato
Mocha
Foundation Model 4 Climate Notes
LLM Carbon