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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Cross-Situational Learning Towards Robot Grounding OOTA, Subba Reddy; Alexandre, Frédéric; Hinaut, Xavier In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022) BASE Show details 2 Cross-Situational Learning Towards Robot Grounding OOTA, Subba Reddy; Alexandre, Frédéric; Hinaut, Xavier In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022) BASE Show details 3 Cross-view Brain Decoding ... Oota, Subba Reddy; Arora, Jashn; Gupta, Manish. - : arXiv, 2022 BASE Show details 4 Visio-Linguistic Brain Encoding ... Oota, Subba Reddy; Arora, Jashn; Rowtula, Vijay; Gupta, Manish; Bapi, Raju S.. - : arXiv, 2022 Abstract: Enabling effective brain-computer interfaces requires understanding how the human brain encodes stimuli across modalities such as visual, language (or text), etc. Brain encoding aims at constructing fMRI brain activity given a stimulus. There exists a plethora of neural encoding models which study brain encoding for single mode stimuli: visual (pretrained CNNs) or text (pretrained language models). Few recent papers have also obtained separate visual and text representation models and performed late-fusion using simple heuristics. However, previous work has failed to explore: (a) the effectiveness of image Transformer models for encoding visual stimuli, and (b) co-attentive multi-modal modeling for visual and text reasoning. In this paper, we systematically explore the efficacy of image Transformers (ViT, DEiT, and BEiT) and multi-modal Transformers (VisualBERT, LXMERT, and CLIP) for brain encoding. Extensive experiments on two popular datasets, BOLD5000 and Pereira, provide the following insights. (1) To ... : 18 pages, 13 figures ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Biological sciences; FOS Computer and information sciences; Machine Learning cs.LG; Neurons and Cognition q-bio.NC URL: https://arxiv.org/abs/2204.08261 https://dx.doi.org/10.48550/arxiv.2204.08261 BASE Hide details 5 Learning to Parse Sentences with Cross-Situational Learning using Different Word Embeddings Towards Robot Grounding ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Alexandre, Frédéric; Hinaut, Xavier. - : Underline Science Inc., 2021 BASE Show details 6 Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts ... Oota, Subba Reddy; Manwani, Naresh; Bapi, Raju S.. - : arXiv, 2019 BASE Show details 7 Mixture of Regression Experts in fMRI Encoding ... Oota, Subba Reddy; Avvaru, Adithya; Manwani, Naresh. - : arXiv, 2018 BASE Show details 8 fMRI Semantic Category Decoding using Linguistic Encoding of Word Embeddings ... Oota, Subba Reddy; Manwani, Naresh; S, Bapi Raju. - : arXiv, 2018 BASE Show details

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