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Model	Arch	License	Params	Seq Len	FP Format	VRAM Infer	Lib	Tokenizer	Comments	Other flavours

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ICML 2023

Wrokshops

• DeepLearning for Code (PC)
• Sparsity in NN as Jeff Dean persuasivly argues large but adaptive and sparsly activated networks both draw on neuroscience-based inspiration and are a good way to control computational costs.

Papers

LLMs on Code

• Planning with Large Language Models for Code Generation, MIT site, code

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{
    "ai_tutor": {
        "Author": "JushBJJ",
        "name": "Mr. Ranedeer",
        "version": "2.3.6",
        "features": {
            "personalization": {
                "depth": {
                    "description": "This is the depth of the content the student wants to learn. A low depth will cover the basics, and generalizations while a high depth will cover the specifics, details, unfamiliar, complex, and side cases. The lowest depth level is 1, and the highest is 10.",
                    "depth_levels": {

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Embeddings for ML4Code papers in TF Projector format

As of 04.2023 we have ~500 paper abstracts + titles in the https://ml4code.github.io/.

These are embeddings for them using 4 different models

• T-SNE GPT-3, model (text-embedding-ada-002)
• T-SNE sentence-transformers/all-MiniLM-L6-v2, model
• [T-SNE allenai/specter2](https://projector.tensorflow.org/?config=https://gist.githubusercontent.com/bzz/a4481b985e690dc937749156f7b851c2/raw/2c39708836065e8d75c479d7802dee0eddc54fe6/specter2-projector_conf

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-4.888096824288368225e-02	2.042284701019525528e-03	-6.076217815279960632e-02	1.794655248522758484e-02	3.518840670585632324e-02	-9.946228563785552979e-02	-4.138710349798202515e-02	2.458935976028442383e-02	-9.456774592399597168e-02	5.039725825190544128e-02	-3.929508477449417114e-02	-7.642178796231746674e-03	5.321704596281051636e-02	3.194081038236618042e-02	6.014326214790344238e-02	8.431854099035263062e-02	-4.898108076304197311e-03	-2.944599604234099388e-03	-1.544282585382461548e-02	-9.158698469400405884e-02	2.330877445638179779e-02	3.664040938019752502e-02	-3.887851536273956299e-02	4.658725485205650330e-02	1.236499520018696785e-03	-2.896820753812789917e-02	-5.904728174209594727e-02	-3.831689059734344482e-02	3.862988203763961792e-02	4.909663111902773380e-04	-9.784634225070476532e-03	1.093933805823326111e-01	-1.426374260336160660e-02	1.259497702121734619e-01	5.247422959655523300e-03	9.101443737745285034e-02	1.028318796306848526e-02	3.331246599555015564e-02	1.219783164560794830e-02	1.863633096218109131e-02	-7.5334

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Controversy on good ideas in technology

As a programmer, it is your job to put yourself out of business. What you do today can be automated tomorrow.

— Douglas McIlroy

1940s computers: digital VS analog

Vannevar Bush, scientist, participant of the Manhattan project, AT&T board of directors, inventor of the Differential Analyzer machine, etc. While he is a conceptual father of “personal computer” or Memex in his As We May Think - The Atlantic - he was famously against digital computers, did not believe it can be built in a reliable way and was in favour of doing analog computations instead.

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ICML 2021

https://icml.cc/Conferences/2021/Schedule 2630 events

Papers

• Leveraging Language to Learn Program Abstractions and Search Heuristics MIT

• How could Neural Networks understand Programs? pdlan/OSCAR? IR

• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis LLVM, Chris Cummins

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Accepted Papers

1. On the Bottleneck of Graph Neural Networks and its Practical Implications

   Uri Alon, Eran Yahav. poster

2. GraphCodeBERT: Pre-training Code Representations with Data Flow

   Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, Ming Zhou. poster

3. BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration

Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai. spotlight

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Paul Graham essays

• Writing
• Startups
  • Funding
    • VCs
    • Investors
  • Founders
  • Hackers
• Silicon Valley

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Essays by Paul Graham

04.03.2021

• Life is Short
• Do Things that Don't Scale
• The Refragmentation