Zobrazeno 1 - 10
of 393
pro vyhledávání: '"Kreiman, Gabriel"'
Autor:
Subramaniam, Vighnesh, Conwell, Colin, Wang, Christopher, Kreiman, Gabriel, Katz, Boris, Cases, Ignacio, Barbu, Andrei
We use (multi)modal deep neural networks (DNNs) to probe for sites of multimodal integration in the human brain by predicting stereoencephalography (SEEG) recordings taken while human subjects watched movies. We operationalize sites of multimodal int
Externí odkaz:
http://arxiv.org/abs/2406.14481
Despite significant advancements in computer vision, understanding complex scenes, particularly those involving humor, remains a substantial challenge. This paper introduces HumorDB, a novel image-only dataset specifically designed to advance visual
Externí odkaz:
http://arxiv.org/abs/2406.13564
Autor:
Madan, Spandan, Xiao, Will, Cao, Mingran, Pfister, Hanspeter, Livingstone, Margaret, Kreiman, Gabriel
We characterized the generalization capabilities of DNN-based encoding models when predicting neuronal responses from the visual cortex. We collected \textit{MacaqueITBench}, a large-scale dataset of neural population responses from the macaque infer
Externí odkaz:
http://arxiv.org/abs/2406.16935
Autor:
Bono, Serena, Madan, Spandan, Grover, Ishaan, Yasueda, Mao, Breazeal, Cynthia, Pfister, Hanspeter, Kreiman, Gabriel
Solutions to Markov Decision Processes (MDP) are often very sensitive to state transition probabilities. As the estimation of these probabilities is often inaccurate in practice, it is important to understand when and how Reinforcement Learning (RL)
Externí odkaz:
http://arxiv.org/abs/2401.15856
Publikováno v:
ICLR 2023
Continual learning is a problem for artificial neural networks that their biological counterparts are adept at solving. Building on work using Sparse Distributed Memory (SDM) to connect a core neural circuit with the powerful Transformer model, we cr
Externí odkaz:
http://arxiv.org/abs/2303.11934
Autor:
Wang, Christopher, Subramaniam, Vighnesh, Yaari, Adam Uri, Kreiman, Gabriel, Katz, Boris, Cases, Ignacio, Barbu, Andrei
We create a reusable Transformer, BrainBERT, for intracranial recordings bringing modern representation learning approaches to neuroscience. Much like in NLP and speech recognition, this Transformer enables classifying complex concepts, i.e., decodin
Externí odkaz:
http://arxiv.org/abs/2302.14367
Autor:
Srinivasan, Ravi, Mignacco, Francesca, Sorbaro, Martino, Refinetti, Maria, Cooper, Avi, Kreiman, Gabriel, Dellaferrera, Giorgia
"Forward-only" algorithms, which train neural networks while avoiding a backward pass, have recently gained attention as a way of solving the biologically unrealistic aspects of backpropagation. Here, we first address compelling challenges related to
Externí odkaz:
http://arxiv.org/abs/2302.05440
Autor:
Singh, Parantak, Li, You, Sikarwar, Ankur, Lei, Weixian, Gao, Daniel, Talbot, Morgan Bruce, Sun, Ying, Shou, Mike Zheng, Kreiman, Gabriel, Zhang, Mengmi
Curriculum design is a fundamental component of education. For example, when we learn mathematics at school, we build upon our knowledge of addition to learn multiplication. These and other concepts must be mastered before our first algebra lesson, w
Externí odkaz:
http://arxiv.org/abs/2211.15470
Visual search is a ubiquitous challenge in natural vision, including daily tasks such as finding a friend in a crowd or searching for a car in a parking lot. Human rely heavily on relevant target features to perform goal-directed visual search. Meanw
Externí odkaz:
http://arxiv.org/abs/2211.13470