#paperthread — Public Fediverse posts

#paperThread #auditory #neuroscience
Our latest paper just came out in the Journal of Neuroscience “Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.” We follow the cortical processing of four different speech-like stimuli (https://dushk88.github.io/progression-of-neural-features) through the brain, using MEG, from early auditory cortex to areas processing semantic-level information. The results show that each language-sensitive processing stage shows both an early (bottom-up-like) cortical contribution and a late (top-down-like) cortical contribution consistent with predictive coding. https://www.jneurosci.org/content/45/11/e1143242025
https://fediscience.org/@jzsimon/111865220831291708

#paperThread #auditory #neuroscience
Our latest paper just came out in the Journal of Neuroscience “Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.” We follow the cortical processing of four different speech-like stimuli (https://dushk88.github.io/progression-of-neural-features) through the brain, using MEG, from early auditory cortex to areas processing semantic-level information. The results show that each language-sensitive processing stage shows both an early (bottom-up-like) cortical contribution and a late (top-down-like) cortical contribution consistent with predictive coding. https://www.jneurosci.org/content/45/11/e1143242025
https://fediscience.org/@jzsimon/111865220831291708

#paperThread #auditory #neuroscience
Our latest paper just came out in the Journal of Neuroscience “Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.” We follow the cortical processing of four different speech-like stimuli (https://dushk88.github.io/progression-of-neural-features) through the brain, using MEG, from early auditory cortex to areas processing semantic-level information. The results show that each language-sensitive processing stage shows both an early (bottom-up-like) cortical contribution and a late (top-down-like) cortical contribution consistent with predictive coding. https://www.jneurosci.org/content/45/11/e1143242025
https://fediscience.org/@jzsimon/111865220831291708

#paperThread #auditory #neuroscience
Our latest paper just came out in the Journal of Neuroscience “Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.” We follow the cortical processing of four different speech-like stimuli (https://dushk88.github.io/progression-of-neural-features) through the brain, using MEG, from early auditory cortex to areas processing semantic-level information. The results show that each language-sensitive processing stage shows both an early (bottom-up-like) cortical contribution and a late (top-down-like) cortical contribution consistent with predictive coding. https://www.jneurosci.org/content/45/11/e1143242025
https://fediscience.org/@jzsimon/111865220831291708

#paperThread #auditory #neuroscience
Our latest paper just came out in the Journal of Neuroscience “Neural Dynamics of the Processing of Speech Features: Evidence for a Progression of Features from Acoustic to Sentential Processing.” We follow the cortical processing of four different speech-like stimuli (https://dushk88.github.io/progression-of-neural-features) through the brain, using MEG, from early auditory cortex to areas processing semantic-level information. The results show that each language-sensitive processing stage shows both an early (bottom-up-like) cortical contribution and a late (top-down-like) cortical contribution consistent with predictive coding. https://www.jneurosci.org/content/45/11/e1143242025
https://fediscience.org/@jzsimon/111865220831291708

#neuroscience #paperThread A new #preprint by Dushyanthi Karunathilake https://doi.org/10.1101/2024.02.02.578603
Language has a hierarchical structure, and some neural processing stages seem to align with these levels. Here we record MEG responses from subjects listening to a progression of speech/speech-like passages: speech-modulated noise; non-words with well-formed phonemes; shuffled words; and true narrative. We can then trace the hierarchy of neural processing stages, from acoustical to full language. 1/7

#neuroscience #paperThread A new #preprint by Dushyanthi Karunathilake https://doi.org/10.1101/2024.02.02.578603
Language has a hierarchical structure, and some neural processing stages seem to align with these levels. Here we record MEG responses from subjects listening to a progression of speech/speech-like passages: speech-modulated noise; non-words with well-formed phonemes; shuffled words; and true narrative. We can then trace the hierarchy of neural processing stages, from acoustical to full language. 1/7

#neuroscience #paperThread A new #preprint by Dushyanthi Karunathilake https://doi.org/10.1101/2024.02.02.578603
Language has a hierarchical structure, and some neural processing stages seem to align with these levels. Here we record MEG responses from subjects listening to a progression of speech/speech-like passages: speech-modulated noise; non-words with well-formed phonemes; shuffled words; and true narrative. We can then trace the hierarchy of neural processing stages, from acoustical to full language. 1/7

#neuroscience #paperThread A new #preprint by Dushyanthi Karunathilake https://doi.org/10.1101/2024.02.02.578603
Language has a hierarchical structure, and some neural processing stages seem to align with these levels. Here we record MEG responses from subjects listening to a progression of speech/speech-like passages: speech-modulated noise; non-words with well-formed phonemes; shuffled words; and true narrative. We can then trace the hierarchy of neural processing stages, from acoustical to full language. 1/7

#neuroscience #paperThread A new #preprint by Dushyanthi Karunathilake https://doi.org/10.1101/2024.02.02.578603
Language has a hierarchical structure, and some neural processing stages seem to align with these levels. Here we record MEG responses from subjects listening to a progression of speech/speech-like passages: speech-modulated noise; non-words with well-formed phonemes; shuffled words; and true narrative. We can then trace the hierarchy of neural processing stages, from acoustical to full language. 1/7

#neuroscience #paperThread New paper in PNAS by recent PhD Dushyanthi Karunathilake! https://doi.org/10.1073/pnas.2309166120
MEG responses from continuous speech listening lock to various stimulus features: acoustic, phonemic, lexical, & semantic. Could they provide an objective measure of when degraded speech is perceived as actually intelligible? This would give insight as to how the brain turns speech into language, and be a treasure mine for clinical populations ill-suited for behavioral testing. 1/5

#neuroscience #paperThread New paper in PNAS by recent PhD Dushyanthi Karunathilake! https://doi.org/10.1073/pnas.2309166120
MEG responses from continuous speech listening lock to various stimulus features: acoustic, phonemic, lexical, & semantic. Could they provide an objective measure of when degraded speech is perceived as actually intelligible? This would give insight as to how the brain turns speech into language, and be a treasure mine for clinical populations ill-suited for behavioral testing. 1/5

#neuroscience #paperThread New paper in PNAS by recent PhD Dushyanthi Karunathilake! https://doi.org/10.1073/pnas.2309166120
MEG responses from continuous speech listening lock to various stimulus features: acoustic, phonemic, lexical, & semantic. Could they provide an objective measure of when degraded speech is perceived as actually intelligible? This would give insight as to how the brain turns speech into language, and be a treasure mine for clinical populations ill-suited for behavioral testing. 1/5

#neuroscience #paperThread New paper in PNAS by recent PhD Dushyanthi Karunathilake! https://doi.org/10.1073/pnas.2309166120
MEG responses from continuous speech listening lock to various stimulus features: acoustic, phonemic, lexical, & semantic. Could they provide an objective measure of when degraded speech is perceived as actually intelligible? This would give insight as to how the brain turns speech into language, and be a treasure mine for clinical populations ill-suited for behavioral testing. 1/5

#neuroscience #paperThread New paper in PNAS by recent PhD Dushyanthi Karunathilake! https://doi.org/10.1073/pnas.2309166120
MEG responses from continuous speech listening lock to various stimulus features: acoustic, phonemic, lexical, & semantic. Could they provide an objective measure of when degraded speech is perceived as actually intelligible? This would give insight as to how the brain turns speech into language, and be a treasure mine for clinical populations ill-suited for behavioral testing. 1/5

Interesting developments in subquadratic alternatives to self-attention based transformers for large sequence modeling (32k and more).

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://arxiv.org/abs/2302.10866

They propose to replace the quadratic self-attention layers by an operator built with implicitly parametrized long kernel 1D convolutions.

#DeepLearning #LLMs #PaperThread

1/4

Interesting developments in subquadratic alternatives to self-attention based transformers for large sequence modeling (32k and more).

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://arxiv.org/abs/2302.10866

They propose to replace the quadratic self-attention layers by an operator built with implicitly parametrized long kernel 1D convolutions.

#DeepLearning #LLMs #PaperThread

1/4

Interesting developments in subquadratic alternatives to self-attention based transformers for large sequence modeling (32k and more).

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://arxiv.org/abs/2302.10866

They propose to replace the quadratic self-attention layers by an operator built with implicitly parametrized long kernel 1D convolutions.

#DeepLearning #LLMs #PaperThread

1/4

Interesting developments in subquadratic alternatives to self-attention based transformers for large sequence modeling (32k and more).

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://arxiv.org/abs/2302.10866

They propose to replace the quadratic self-attention layers by an operator built with implicitly parametrized long kernel 1D convolutions.

#DeepLearning #LLMs #PaperThread

1/4

Interesting developments in subquadratic alternatives to self-attention based transformers for large sequence modeling (32k and more).

Hyena Hierarchy: Towards Larger Convolutional Language Models

https://arxiv.org/abs/2302.10866

They propose to replace the quadratic self-attention layers by an operator built with implicitly parametrized long kernel 1D convolutions.

#DeepLearning #LLMs #PaperThread

1/4

It's not the first time! A dream team of Eve Fleisig (human eval), Adam Lopez (remembers the Stat MT era), Kyunghyun Cho (helped end it), and me (pun in title) are here to teach you the history of scale crises and what lessons we can take from them. https://arxiv.org/abs/2311.05020 🧵 #paperthread #LLMs

It's not the first time! A dream team of Eve Fleisig (human eval), Adam Lopez (remembers the Stat MT era), Kyunghyun Cho (helped end it), and me (pun in title) are here to teach you the history of scale crises and what lessons we can take from them. https://arxiv.org/abs/2311.05020 🧵 #paperthread #LLMs

It's not the first time! A dream team of Eve Fleisig (human eval), Adam Lopez (remembers the Stat MT era), Kyunghyun Cho (helped end it), and me (pun in title) are here to teach you the history of scale crises and what lessons we can take from them. https://arxiv.org/abs/2311.05020 🧵 #paperthread #LLMs

It's not the first time! A dream team of Eve Fleisig (human eval), Adam Lopez (remembers the Stat MT era), Kyunghyun Cho (helped end it), and me (pun in title) are here to teach you the history of scale crises and what lessons we can take from them. https://arxiv.org/abs/2311.05020 🧵 #paperthread #LLMs

It's not the first time! A dream team of Eve Fleisig (human eval), Adam Lopez (remembers the Stat MT era), Kyunghyun Cho (helped end it), and me (pun in title) are here to teach you the history of scale crises and what lessons we can take from them. https://arxiv.org/abs/2311.05020 🧵 #paperthread #LLMs

📝 Now reading: "From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences -- by @fedeadolfi #LauraVandeBraak, and @mariekewoe (2023, PsyArXiv) #PaperThread 🧵

https://doi.org/10.31234/osf.io/jthxf

📝 Now reading: "From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences -- by @fedeadolfi #LauraVandeBraak, and @mariekewoe (2023, PsyArXiv) #PaperThread 🧵

https://doi.org/10.31234/osf.io/jthxf

📝 Now reading: "From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences -- by @fedeadolfi #LauraVandeBraak, and @mariekewoe (2023, PsyArXiv) #PaperThread 🧵

https://doi.org/10.31234/osf.io/jthxf

📝 Now reading: "From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences -- by @fedeadolfi #LauraVandeBraak, and @mariekewoe (2023, PsyArXiv) #PaperThread 🧵

https://doi.org/10.31234/osf.io/jthxf

📝 Now reading: "From empirical problem-solving to theoretical problem-finding perspectives on the cognitive sciences -- by @fedeadolfi #LauraVandeBraak, and @mariekewoe (2023, PsyArXiv) #PaperThread 🧵

https://doi.org/10.31234/osf.io/jthxf

@jkanev I follow #NewPaper OR #preprint OR #PaperThread and it's *very* quiet.

@jkanev I follow #NewPaper OR #preprint OR #PaperThread and it's *very* quiet.

@jkanev I follow #NewPaper OR #preprint OR #PaperThread and it's *very* quiet.

@jkanev I follow #NewPaper OR #preprint OR #PaperThread and it's *very* quiet.

@jkanev I follow #NewPaper OR #preprint OR #PaperThread and it's *very* quiet.

DINOv2: Learning Robust Visual Features without Supervision

Tricks applied to DINO and iBOT to learn robust/generic features for many downstream tasks

My summary on HFPapers: https://huggingface.co/papers/2304.07193#64f81a7f35a0a9fc54363373
arXiv: https://arxiv.org/abs/2304.07193
Demo: https://dinov2.metademolab.com/

#arXiv #PaperThread #FoundationModels

DINOv2: Learning Robust Visual Features without Supervision

Tricks applied to DINO and iBOT to learn robust/generic features for many downstream tasks

My summary on HFPapers: https://huggingface.co/papers/2304.07193#64f81a7f35a0a9fc54363373
arXiv: https://arxiv.org/abs/2304.07193
Demo: https://dinov2.metademolab.com/

#arXiv #PaperThread #FoundationModels

DINOv2: Learning Robust Visual Features without Supervision

Tricks applied to DINO and iBOT to learn robust/generic features for many downstream tasks

My summary on HFPapers: https://huggingface.co/papers/2304.07193#64f81a7f35a0a9fc54363373
arXiv: https://arxiv.org/abs/2304.07193
Demo: https://dinov2.metademolab.com/

#arXiv #PaperThread #FoundationModels

DINOv2: Learning Robust Visual Features without Supervision

Tricks applied to DINO and iBOT to learn robust/generic features for many downstream tasks

My summary on HFPapers: https://huggingface.co/papers/2304.07193#64f81a7f35a0a9fc54363373
arXiv: https://arxiv.org/abs/2304.07193
Demo: https://dinov2.metademolab.com/

#arXiv #PaperThread #FoundationModels

DINOv2: Learning Robust Visual Features without Supervision

Tricks applied to DINO and iBOT to learn robust/generic features for many downstream tasks

My summary on HFPapers: https://huggingface.co/papers/2304.07193#64f81a7f35a0a9fc54363373
arXiv: https://arxiv.org/abs/2304.07193
Demo: https://dinov2.metademolab.com/

#arXiv #PaperThread #FoundationModels

LidarCLIP or: How I Learned to Talk to Point Clouds

Align LiDAR encoder to CLIP image encoder and you can query LiDAR through image similarity or even text.

My summary on HFPapers: https://huggingface.co/papers/2212.06858#64f6f05910a91217c38874b7
arXiv: https://arxiv.org/abs/2212.06858
PWC: https://paperswithcode.com/paper/lidarclip-or-how-i-learned-to-talk-to-point

#arXiv #PaperThread #FoundationModels

LidarCLIP or: How I Learned to Talk to Point Clouds

Align LiDAR encoder to CLIP image encoder and you can query LiDAR through image similarity or even text.

My summary on HFPapers: https://huggingface.co/papers/2212.06858#64f6f05910a91217c38874b7
arXiv: https://arxiv.org/abs/2212.06858
PWC: https://paperswithcode.com/paper/lidarclip-or-how-i-learned-to-talk-to-point

#arXiv #PaperThread #FoundationModels

LidarCLIP or: How I Learned to Talk to Point Clouds

Align LiDAR encoder to CLIP image encoder and you can query LiDAR through image similarity or even text.

My summary on HFPapers: https://huggingface.co/papers/2212.06858#64f6f05910a91217c38874b7
arXiv: https://arxiv.org/abs/2212.06858
PWC: https://paperswithcode.com/paper/lidarclip-or-how-i-learned-to-talk-to-point

#arXiv #PaperThread #FoundationModels

LidarCLIP or: How I Learned to Talk to Point Clouds

Align LiDAR encoder to CLIP image encoder and you can query LiDAR through image similarity or even text.

My summary on HFPapers: https://huggingface.co/papers/2212.06858#64f6f05910a91217c38874b7
arXiv: https://arxiv.org/abs/2212.06858
PWC: https://paperswithcode.com/paper/lidarclip-or-how-i-learned-to-talk-to-point

#arXiv #PaperThread #FoundationModels

LidarCLIP or: How I Learned to Talk to Point Clouds

Align LiDAR encoder to CLIP image encoder and you can query LiDAR through image similarity or even text.

My summary on HFPapers: https://huggingface.co/papers/2212.06858#64f6f05910a91217c38874b7
arXiv: https://arxiv.org/abs/2212.06858
PWC: https://paperswithcode.com/paper/lidarclip-or-how-i-learned-to-talk-to-point

#arXiv #PaperThread #FoundationModels

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Top-view and ground-view images can make neural maps that aid visual positioning.

My summary on HFPapers: https://huggingface.co/papers/2306.05407#64c3fcb171947b03ffcf314c
arXiv: https://arxiv.org/abs/2306.05407
PapersWithCode: https://paperswithcode.com/paper/snap-self-supervised-neural-maps-for-visual

#arXiv #PaperThread #NewPaper #SSL

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Top-view and ground-view images can make neural maps that aid visual positioning.

My summary on HFPapers: https://huggingface.co/papers/2306.05407#64c3fcb171947b03ffcf314c
arXiv: https://arxiv.org/abs/2306.05407
PapersWithCode: https://paperswithcode.com/paper/snap-self-supervised-neural-maps-for-visual

#arXiv #PaperThread #NewPaper #SSL

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Top-view and ground-view images can make neural maps that aid visual positioning.

My summary on HFPapers: https://huggingface.co/papers/2306.05407#64c3fcb171947b03ffcf314c
arXiv: https://arxiv.org/abs/2306.05407
PapersWithCode: https://paperswithcode.com/paper/snap-self-supervised-neural-maps-for-visual

#arXiv #PaperThread #NewPaper #SSL

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Top-view and ground-view images can make neural maps that aid visual positioning.

My summary on HFPapers: https://huggingface.co/papers/2306.05407#64c3fcb171947b03ffcf314c
arXiv: https://arxiv.org/abs/2306.05407
PapersWithCode: https://paperswithcode.com/paper/snap-self-supervised-neural-maps-for-visual

#arXiv #PaperThread #NewPaper #SSL

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding

Top-view and ground-view images can make neural maps that aid visual positioning.

My summary on HFPapers: https://huggingface.co/papers/2306.05407#64c3fcb171947b03ffcf314c
arXiv: https://arxiv.org/abs/2306.05407
PapersWithCode: https://paperswithcode.com/paper/snap-self-supervised-neural-maps-for-visual

#arXiv #PaperThread #NewPaper #SSL