Zobrazeno 1 - 10
of 19
pro vyhledávání: '"He, Horace"'
Autor:
Black, Sid, Biderman, Stella, Hallahan, Eric, Anthony, Quentin, Gao, Leo, Golding, Laurence, He, Horace, Leahy, Connor, McDonell, Kyle, Phang, Jason, Pieler, Michael, Prashanth, USVSN Sai, Purohit, Shivanshu, Reynolds, Laria, Tow, Jonathan, Wang, Ben, Weinbach, Samuel
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model trained on the Pile, whose weights will be made freely and openly available to the public through a permissive license. It is, to the best of our knowledge, the largest d
Externí odkaz:
http://arxiv.org/abs/2204.06745
Modern deep learning frameworks provide imperative, eager execution programming interfaces embedded in Python to provide a productive development experience. However, deep learning practitioners sometimes need to capture and transform program structu
Externí odkaz:
http://arxiv.org/abs/2112.08429
Autor:
Singh, Abhay, Huang, Qian, Huang, Sijia Linda, Bhalerao, Omkar, He, Horace, Lim, Ser-Nam, Benson, Austin R.
Graphs are a common model for complex relational data such as social networks and protein interactions, and such data can evolve over time (e.g., new friendships) and be noisy (e.g., unmeasured interactions). Link prediction aims to predict future ed
Externí odkaz:
http://arxiv.org/abs/2106.15810
Autor:
Hendrycks, Dan, Basart, Steven, Kadavath, Saurav, Mazeika, Mantas, Arora, Akul, Guo, Ethan, Burns, Collin, Puranik, Samir, He, Horace, Song, Dawn, Steinhardt, Jacob
While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importance, there has been surprisingly little work on evaluating code generat
Externí odkaz:
http://arxiv.org/abs/2105.09938
Autor:
Gao, Leo, Biderman, Stella, Black, Sid, Golding, Laurence, Hoppe, Travis, Foster, Charles, Phang, Jason, He, Horace, Thite, Anish, Nabeshima, Noa, Presser, Shawn, Leahy, Connor
Recent work has demonstrated that increased training dataset diversity improves general cross-domain knowledge and downstream generalization capability for large-scale language models. With this in mind, we present \textit{the Pile}: an 825 GiB Engli
Externí odkaz:
http://arxiv.org/abs/2101.00027
As machine learning techniques become ubiquitous, the efficiency of neural network implementations is becoming correspondingly paramount. Frameworks, such as Halide and TVM, separate out the algorithmic representation of the network from the schedule
Externí odkaz:
http://arxiv.org/abs/2011.14486
Graph Neural Networks (GNNs) are the predominant technique for learning over graphs. However, there is relatively little understanding of why GNNs are successful in practice and whether they are necessary for good performance. Here, we show that for
Externí odkaz:
http://arxiv.org/abs/2010.13993
Incorporating relational reasoning into neural networks has greatly expanded their capabilities and scope. One defining trait of relational reasoning is that it operates on a set of entities, as opposed to standard vector representations. Existing en
Externí odkaz:
http://arxiv.org/abs/2003.04448
Neural networks are vulnerable to adversarial examples, malicious inputs crafted to fool trained models. Adversarial examples often exhibit black-box transfer, meaning that adversarial examples for one model can fool another model. However, adversari
Externí odkaz:
http://arxiv.org/abs/1907.10823
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of th
Externí odkaz:
http://arxiv.org/abs/1812.01198