Model | Arch | License | Params | Seq Len | FP Format | VRAM Infer | Lib | Tokenizer | Comments | Other flavours |
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- 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.
{ | |
"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": { |
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
-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 |
As a programmer, it is your job to put yourself out of business. What you do today can be automated tomorrow.
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.
https://icml.cc/Conferences/2021/Schedule 2630 events
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On the Bottleneck of Graph Neural Networks and its Practical Implications
Uri Alon, Eran Yahav. poster
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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
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BUSTLE: Bottom-Up Program Synthesis Through Learning-Guided Exploration
Augustus Odena, Kensen Shi, David Bieber, Rishabh Singh, Charles Sutton, Hanjun Dai. spotlight
04.03.2021