Zobrazeno 1 - 10
of 37 039
pro vyhledávání: '"Neel, A."'
Autor:
Lieberum, Tom, Rajamanoharan, Senthooran, Conmy, Arthur, Smith, Lewis, Sonnerat, Nicolas, Varma, Vikrant, Kramár, János, Dragan, Anca, Shah, Rohin, Nanda, Neel
Sparse autoencoders (SAEs) are an unsupervised method for learning a sparse decomposition of a neural network's latent representations into seemingly interpretable features. Despite recent excitement about their potential, research applications outsi
Externí odkaz:
http://arxiv.org/abs/2408.05147
Autor:
Chen, Yiming, D'Souza, Niharika S., Mandepally, Akshith, Henninger, Patrick, Kashyap, Satyananda, Karani, Neerav, Dey, Neel, Zachary, Marcos, Rizq, Raed, Chouinard, Paul, Golland, Polina, Syeda-Mahmood, Tanveer F.
Precisely estimating lumen boundaries in intravascular ultrasound (IVUS) is needed for sizing interventional stents to treat deep vein thrombosis (DVT). Unfortunately, current segmentation networks like the UNet lack the precision needed for clinical
Externí odkaz:
http://arxiv.org/abs/2408.04826
Autor:
Reuel, Anka, Bucknall, Ben, Casper, Stephen, Fist, Tim, Soder, Lisa, Aarne, Onni, Hammond, Lewis, Ibrahim, Lujain, Chan, Alan, Wills, Peter, Anderljung, Markus, Garfinkel, Ben, Heim, Lennart, Trask, Andrew, Mukobi, Gabriel, Schaeffer, Rylan, Baker, Mauricio, Hooker, Sara, Solaiman, Irene, Luccioni, Alexandra Sasha, Rajkumar, Nitarshan, Moës, Nicolas, Ladish, Jeffrey, Guha, Neel, Newman, Jessica, Bengio, Yoshua, South, Tobin, Pentland, Alex, Koyejo, Sanmi, Kochenderfer, Mykel J., Trager, Robert
AI progress is creating a growing range of risks and opportunities, but it is often unclear how they should be navigated. In many cases, the barriers and uncertainties faced are at least partly technical. Technical AI governance, referring to technic
Externí odkaz:
http://arxiv.org/abs/2407.14981
Autor:
Rajamanoharan, Senthooran, Lieberum, Tom, Sonnerat, Nicolas, Conmy, Arthur, Varma, Vikrant, Kramár, János, Nanda, Neel
Sparse autoencoders (SAEs) are a promising unsupervised approach for identifying causally relevant and interpretable linear features in a language model's (LM) activations. To be useful for downstream tasks, SAEs need to decompose LM activations fait
Externí odkaz:
http://arxiv.org/abs/2407.14435
The use of quantum cryptography in everyday applications has gained attention in both industrial and academic fields. Due to advancements in quantum electronics, practical quantum devices are already available in the market, and ready for wider use.
Externí odkaz:
http://arxiv.org/abs/2407.13156
The evolution of science education is a dynamic process driven by advances in pedagogy, technology, and especially, our understanding of how students learn. Educators are exploring innovative teaching and learning methodologies such as active learnin
Externí odkaz:
http://arxiv.org/abs/2407.09330
Autor:
White, Colin, Dooley, Samuel, Roberts, Manley, Pal, Arka, Feuer, Ben, Jain, Siddhartha, Shwartz-Ziv, Ravid, Jain, Neel, Saifullah, Khalid, Naidu, Siddartha, Hegde, Chinmay, LeCun, Yann, Goldstein, Tom, Neiswanger, Willie, Goldblum, Micah
Test set contamination, wherein test data from a benchmark ends up in a newer model's training set, is a well-documented obstacle for fair LLM evaluation and can quickly render benchmarks obsolete. To mitigate this, many recent benchmarks crowdsource
Externí odkaz:
http://arxiv.org/abs/2406.19314
Autor:
Vora, Neel R., Xu, Yilun, Hashim, Akel, Fruitwala, Neelay, Nguyen, Ho Nam, Liao, Haoran, Balewski, Jan, Rajagopala, Abhi, Nowrouzi, Kasra, Ji, Qing, Whaley, K. Birgitta, Siddiqi, Irfan, Nguyen, Phuc, Huang, Gang
Similar to reading the transistor state in classical computers, identifying the quantum bit (qubit) state is a fundamental operation to translate quantum information. However, identifying quantum state has been the slowest and most susceptible to err
Externí odkaz:
http://arxiv.org/abs/2406.18807
Decomposing model activations into interpretable components is a key open problem in mechanistic interpretability. Sparse autoencoders (SAEs) are a popular method for decomposing the internal activations of trained transformers into sparse, interpret
Externí odkaz:
http://arxiv.org/abs/2406.17759
We revisit the efficacy of several practical methods for approximate machine unlearning developed for large-scale deep learning. In addition to complying with data deletion requests, one often-cited potential application for unlearning methods is to
Externí odkaz:
http://arxiv.org/abs/2406.17216