Zobrazeno 1 - 10
of 25 757
pro vyhledávání: '"A. Hendrix"'
Autor:
Ray, Arijit, Duan, Jiafei, Tan, Reuben, Bashkirova, Dina, Hendrix, Rose, Ehsani, Kiana, Kembhavi, Aniruddha, Plummer, Bryan A., Krishna, Ranjay, Zeng, Kuo-Hao, Saenko, Kate
Spatial perception is a fundamental component of intelligence. While many studies highlight that large multimodal language models (MLMs) struggle to reason about space, they only test for static spatial reasoning, such as categorizing the relative po
Externí odkaz:
http://arxiv.org/abs/2412.07755
Autor:
Hendrix, Rutger, Salanitri, Federica Proietto, Spampinato, Concetto, Palazzo, Simone, Bagci, Ulas
We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts
Externí odkaz:
http://arxiv.org/abs/2411.10071
Autor:
Hendrix, Zachary H., Brown, Peter T., Flanagan, Tim, Shepherd, Douglas P., Saurabh, Ayush, Pressé, Steve
Richardson-Lucy deconvolution is widely used to restore images from degradation caused by the broadening effects of a point spread function and corruption by photon shot noise, in order to recover an underlying object. In practice, this is achieved b
Externí odkaz:
http://arxiv.org/abs/2411.00991
Autor:
Pernsteiner, Stuart, Diatchki, Iavor S., Dockins, Robert, Dodds, Mike, Hendrix, Joe, Ravich, Tristan, Redmond, Patrick, Scott, Ryan, Tomb, Aaron
We present Crux, a cross-language verification tool for Rust and C/LLVM. Crux targets bounded, intricate pieces of code that are difficult for humans to get right: for example, cryptographic modules and serializer / deserializer pairs. Crux builds on
Externí odkaz:
http://arxiv.org/abs/2410.18280
Autor:
Deitke, Matt, Clark, Christopher, Lee, Sangho, Tripathi, Rohun, Yang, Yue, Park, Jae Sung, Salehi, Mohammadreza, Muennighoff, Niklas, Lo, Kyle, Soldaini, Luca, Lu, Jiasen, Anderson, Taira, Bransom, Erin, Ehsani, Kiana, Ngo, Huong, Chen, YenSung, Patel, Ajay, Yatskar, Mark, Callison-Burch, Chris, Head, Andrew, Hendrix, Rose, Bastani, Favyen, VanderBilt, Eli, Lambert, Nathan, Chou, Yvonne, Chheda, Arnavi, Sparks, Jenna, Skjonsberg, Sam, Schmitz, Michael, Sarnat, Aaron, Bischoff, Byron, Walsh, Pete, Newell, Chris, Wolters, Piper, Gupta, Tanmay, Zeng, Kuo-Hao, Borchardt, Jon, Groeneveld, Dirk, Nam, Crystal, Lebrecht, Sophie, Wittlif, Caitlin, Schoenick, Carissa, Michel, Oscar, Krishna, Ranjay, Weihs, Luca, Smith, Noah A., Hajishirzi, Hannaneh, Girshick, Ross, Farhadi, Ali, Kembhavi, Aniruddha
Today's most advanced vision-language models (VLMs) remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling these closed VLMs into open ones. As a r
Externí odkaz:
http://arxiv.org/abs/2409.17146
Autor:
Hu, Jiaheng, Hendrix, Rose, Farhadi, Ali, Kembhavi, Aniruddha, Martin-Martin, Roberto, Stone, Peter, Zeng, Kuo-Hao, Ehsani, Kiana
In recent years, the Robotics field has initiated several efforts toward building generalist robot policies through large-scale multi-task Behavior Cloning. However, direct deployments of these policies have led to unsatisfactory performance, where t
Externí odkaz:
http://arxiv.org/abs/2409.16578
Autor:
Balve, Ann-Kristin, Hendrix, Peter
Publikováno v:
Proceedings of Machine Learning Research, Vol. 248, 2024
Deep learning models have achieved promising results in breast cancer classification, yet their 'black-box' nature raises interpretability concerns. This research addresses the crucial need to gain insights into the decision-making process of convolu
Externí odkaz:
http://arxiv.org/abs/2408.13154
Autor:
Scott, Ryan G., Boston, Brett, Davis, Benjamin, Diatchki, Iavor, Dodds, Mike, Hendrix, Joe, Matichuk, Daniel, Quick, Kevin, Ravitch, Tristan, Robert, Valentin, Selfridge, Benjamin, Stefănescu, Andrei, Wagner, Daniel, Winwood, Simon
When attempting to understand the behavior of an executable, a binary analyst can make use of many different techniques. These include program slicing, dynamic instrumentation, binary-level rewriting, symbolic execution, and formal verification, all
Externí odkaz:
http://arxiv.org/abs/2407.06375
Autor:
Sogancioglu, Ecem, van Ginneken, Bram, Behrendt, Finn, Bengs, Marcel, Schlaefer, Alexander, Radu, Miron, Xu, Di, Sheng, Ke, Scalzo, Fabien, Marcus, Eric, Papa, Samuele, Teuwen, Jonas, Scholten, Ernst Th., Schalekamp, Steven, Hendrix, Nils, Jacobs, Colin, Hendrix, Ward, Sánchez, Clara I, Murphy, Keelin
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lu
Externí odkaz:
http://arxiv.org/abs/2401.02192
Autor:
Zeng, Kuo-Hao, Zhang, Zichen, Ehsani, Kiana, Hendrix, Rose, Salvador, Jordi, Herrasti, Alvaro, Girshick, Ross, Kembhavi, Aniruddha, Weihs, Luca
We present PoliFormer (Policy Transformer), an RGB-only indoor navigation agent trained end-to-end with reinforcement learning at scale that generalizes to the real-world without adaptation despite being trained purely in simulation. PoliFormer uses
Externí odkaz:
http://arxiv.org/abs/2406.20083