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Lenny van Dyck

@levandyck.bsky.social
221 followers 283 following 27 posts

PhD candidate in CogCompNeuro at JLU Giessen Exploring brains, minds, and worlds 🧠💭🗺️ levandyck.github.io

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Reposted by Lenny van Dyck
Earl K. Miller @earlkmiller.bsky.social · 14/09/2026
Most cortical neurons exhibit mixed selectivity rather than specializing in a single function. Our limited ability to juggle tasks may therefore reflect interference among overlapping neural representations. www.nature.com/articles/s41... #neuroscience
nature.com
Feature interference underlies a neuronal basis for the behavioral cost of task uncertainty - Nature Neuroscience
Using a combined approach of monkey electrophysiology, artificial neural networks and human psychophysics, Xue et al. show that neuronal interference from irrelevant information underlies behavioral e...
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Rhodri Cusack @rhodricusack.bsky.social · 10/09/2026
We're hiring! 2 postdocs + 3 PhD students to join InfantNeuroAI at Trinity College Dublin: awake infant fMRI & OPM-MEG, plus computational models of how infants learn. Aims: understand infant visual cognition, infant neuroimaging methods, more energy-efficient learning 🧵 www.cusacklab.org/vacancies
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Rob Mok @robmok.bsky.social · 30/08/2026
📢 Announcing the 11th CiNet Conference: "From Biological to General Artificial Intelligence: Computational and Experimental Approaches to Understanding and Modeling the Brain" 5–7 October 2026, CiNet, Osaka, Japan Organized by yours truly & Shinji Nishimoto #neuroskyence #psychscisky #mlsky 🧠🤖 1/
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Davide Cortinovis @davidecortinovis.bsky.social · 28/08/2026
Final work from my PhD is out as a preprint, my last project at Trento before moving to Western! www.biorxiv.org/content/10.6... In this work, we ask: how is food represented in visual cortex?🧵
biorxiv.org
Object dimensions underlying food representations in visual cortex
Recent work has revealed two food-selective areas in the lateral and ventromedial occipitotemporal cortex (OTC). These studies have shown that food selectivity in these regions cannot be explained by ...
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Martin Hebart @martinhebart.bsky.social · 24/08/2026
I have been meaning to write a post about a preprint that I'm pretty excited about: www.biorxiv.org/content/10.6... In neuroscience we often assume that we can learn something general about “the brain” using different species. But is this true? Do monkeys even see the world the way we do?
On the left, an image of a rhesus macaque looking towards its left, and on the right, an AI-generated zoomed-out rendering of a human in the same pose. Both are looking.
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Tom McCoy @rtommccoy.bsky.social · 20/08/2026
Since many are starting grad school soon, let me re-share my One Big Tip™️ for research! Research involves many skills - collaborating, writing, presenting, etc. But many of these skills can be unified under a single overarching ability: theory of mind Blog post link in reply
Illustration of the blog post's main argument, summarized as: "Theory of Mind as a Central Skill for Researchers: Research involves many skills.If each skill is viewed separately, each one takes a long time to learn. These skills can instead be connected via theory of mind – the ability to reason about the mental states of others. This allows you to transfer your abilities across areas, making it easier to gain new skills."
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Sander van Bree @sandervanbree.bsky.social · 12/08/2026
Excited to share our preprint! w/ @martinhebart.bsky.social To understand how primates visually process objects in the world, we rely on both research in human and macaque IT. But what representations of object space are actually shared between them? biorxiv.org/content/10.6... Quick thread 🧵
biorxiv.org
Shared and Distinct High-Dimensional Object Spaces in Human and Macaque Inferotemporal Cortex
Human and macaque studies of inferotemporal cortex (IT) have shaped our understanding of object vision, yet the extent of their representational alignment and the precise nature of this correspondence...
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Lenny van Dyck @levandyck.bsky.social · 05/08/2026
And since we're at CCN, here's a little preview of all the models we tested so far 🚀
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Lenny van Dyck @levandyck.bsky.social · 05/08/2026
Excited to be at #CCN2026 in New York to present brand-new work with @kathadobs.bsky.social 🥳 By testing hundreds of categories, we found striking differences in functional specialization between visual cortex and DNNs 🧠 Come find me at poster F81 on Thursday 1:45-3:30 p.m. Happy to chat!
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Stanford Vision and Perception Neuroscience Lab @stanfordvpnl.bsky.social · 04/08/2026
VPNL is at #CCN2026 !! Check out our lab members' presentations this week :)
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Katharina Dobs @kathadobs.bsky.social · 03/08/2026
While I'm sad to miss #CCN2026, you don't have to miss out on the cool work coming out of the lab. 😊 Super proud of the diversity of projects this year—from category and unit specialization to inversion effects, shape bias, and memorability in brains and DNNs! Stop by and check them out! #NeuroAI
Graphic showing the VCCN Lab’s contributions to CCN 2026. The design features the VCCN Lab logo on the left, a stylized neural network icon in the center, and “CCN 2026” on the right. Five posters are listed, each accompanied by a headshot of the presenting researcher and the scheduled poster session.
Jakob Winkler: Rethinking the inversion effect as a graded phenomenon across object categories (Poster Session A, Tuesday, August 4, 9:30–11:15 am).
Sule Tasliyurt-Celebi: Memorability varies with how visual information is sampled by humans and models (Poster Session C, Wednesday, August 5, 9:30–11:15 am).
Alban Flachot: More shape bias does not mean more human-like visual representations (Poster Session C, Wednesday, August 5, 9:30–11:15 am).
Lenny van Dyck: Different features shape category selectivity in human visual cortex and DNNs (Poster Session F, Thursday, August 6, 1:45–3:30 pm).
Zhengqing Miao: Functionally specialized units reflect readout pathways rather than distinct feature encoding in artificial neural networks (Poster Session F, Thursday, August 6, 1:45–3:30 pm).
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Vlad Ayzenberg @vayzenb.bsky.social · 22/07/2026
Excited to share our review in @cp-neuron.bsky.social with @lauriebayet.bsky.social and @mickbonner.bsky.social! We describe how implementing principles from child development can advance the mechanistic plausibility and capacities of AI models We packed A LOT into this review, here's a quick 🧵
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Lenny van Dyck @levandyck.bsky.social · 17/07/2026
Thanks! This is actually a journal requirement, not something we added intentionally :)
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Lenny van Dyck @levandyck.bsky.social · 16/07/2026
Excited to share that our paper is now out in #JNeurosci! We propose a multidimensional framework of high-level visual cortex that reconciles a longstanding debate. Thanks to @kathadobs.bsky.social, @martinhebart.bsky.social, and everyone else for the great discussions along the way. More to come 🧠🌈
doi.org
Multidimensional feature tuning in category-selective areas of human visual cortex
Two prominent accounts describe the functional organization of human high-level visual cortex. A categorical view emphasizes category-selective areas, while a dimensional view highlights continuous fe...
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Lorenzo Posani @lorenzoposani.com · 15/07/2026
Out today on @nature.com! Connecting neural specialization, population geometry, and their functional implications across the cortical hierarchy. So grateful for this beautiful and fun collaboration with @shuqiw.bsky.social, S Muscinelli, L Paninski, and @stefanofusi.bsky.social!
nature.com
Rarely categorical, highly separable representations along the cortical hierarchy - Nature
Cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared towards high-dimensional, highly separable neural representations.
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Katharina Dobs @kathadobs.bsky.social · 15/05/2026
It's #VSS2026 time! We're super excited to be back and presenting some exciting, ongoing projects in the lab. Come by and say hi! 🤗
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Evan Gordon @gordonneuro.bsky.social · 22/04/2026
I always assumed that brain function had to line up with cytoarchitectonics. It turns out I was wrong. Human cortex, especially PFC, is tiled by chains of functional patches that subdivide and interlink architectonic areas into parallel processing streams. www.biorxiv.org/content/10.6...
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Rob Mok @robmok.bsky.social · 14/04/2026
Excited to share our new preprint: "Do Machines Fail Like Humans? A Human-Centered Out-of-Distribution Spectrum for Mapping Error Alignment" led by @binxia.bsky.social w @ken-lxl.bsky.social & co-senior author Luke Dickens (UCL) 🤖🧵👇 Link: arxiv.org/abs/2603.07462 🧠📈#PsychSciSky #compneuro #mlsky /1
arxiv.org
Do Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment
Determining whether AI systems process information similarly to humans is central to cognitive science and trustworthy AI. While modern AI models can match human accuracy on standard tasks, such parit...
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Stéphane d’Ascoli @sdascoli.bsky.social · 26/03/2026
🚨 We're very happy to introduce TRIBE v2: a foundation model of the brain's responses to sight, sound & language. 📄 Paper: ai.meta.com/research/pub... ▶️ Demo: aidemos.atmeta.com/tribev2/ 💻 Code: github.com/facebookrese... 🤗 Model: huggingface.co/facebook/tri...
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Katharina Dobs @kathadobs.bsky.social · 26/02/2026
Category selectivity vs. behavioral relevance in visual cortex? 👁️🧠 @levandyck.bsky.social and I really enjoyed diving into this piece and appreciated the authors’ thoughtful response. We're curious to see how the field moves forward from here! ➡️
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J. Brendan Ritchie @jbrendanritchie.bsky.social · 25/02/2026
Our reply to 11 commentaries on our article ("Rethinking category-selectivity in human visual cortex") is out in Cognitive Neuroscience! Thanks to @susanwardle.bsky.social @maryamvaziri.bsky.social Dwight Kravitz @cibaker.bsky.social and all who contributed! 1/x www.tandfonline.com/doi/full/10....
tandfonline.com
What behavioral relevance is (not)
We are thankful for the thoughtful commentaries of our colleagues. In our discussion article, we argued for a course correction to how the field approaches the organization of visual function in oc...
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
To summarize: Both models and cortex show partially integrated face and body representations, which converge along the visual hierarchy. This may strike a balance between segregation and integration, building part-based representations that support the full range of person perception. 🧠 9/9
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
Finally, we examined how DNNs integrate faces and bodies: DNN responses to whole persons approximated the sum of their responses to isolated faces and bodies, suggesting part-based rather than holistic integration. 8/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
Back in DNNs, we tested whether mixed selectivity is functionally relevant: We trained a DNN on different person perception tasks and lesioned each unit type. Lesioning mixed-selective units led to deficits across tasks, confirming these units actively support person perception. 7/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
While face- and body-selective units explained substantial unique variance in their corresponding regions, they mainly explained shared variance. Crucially, this shared component increased from posterior to anterior regions, suggesting that integration grows along the cortical hierarchy. 6/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
Let’s now link this to visual cortex: Mixed-selective units best predicted activity in both face- and body-selective regions, suggesting that they encode more integrated person information than generally assumed. 🪢 5/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
Let’s first look at computational models of visual cortex: Diverse DNNs developed distinct face- and body-selective units, but also mixed-selective units responding to both categories. This suggests that face-body integration naturally arises from learning to recognize whole persons. 🕺 4/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
So, here we ... 1️⃣ Used a functional localizer to test whether DNNs develop distinct and/or shared tuning for faces and bodies. 2️⃣ Applied fMRI encoding models to map DNN representations onto visual cortex. 3️⃣ Closely examined DNN representations to generate hypotheses about face-body integration. 3/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
The brain has specialized regions for faces and bodies. However, in the real world, they usually appear together as whole persons. Whether and how visual cortex integrates face and body representations remains largely unclear. 🤔 2/n
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Lenny van Dyck @levandyck.bsky.social · 24/02/2026
How segregated vs. integrated are face and body representations in human visual cortex? In this new preprint with @kathadobs.bsky.social, we use DNNs and fMRI to find out. www.biorxiv.org/content/10.6... #neuroskyence 🧵 1/n
biorxiv.org
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Stanford Vision and Perception Neuroscience Lab @stanfordvpnl.bsky.social · 20/02/2026
🚨 Jewelia’s new preprint! We report the first pRF mapping in teens + reveal functional fingerprints of category regions in high-level visual cortex. www.biorxiv.org/content/10.6...
biorxiv.org
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Tim Maniquet @tmaniquet.bsky.social · 18/02/2026
🔥 We have a new preprint! 🔥 It's really nice, I recommend reading it Here's a summary question: Can FFA activation predict RT to faces? ⏬ Check out the answer below ⏬ @hansopdebeeck.bsky.social www.biorxiv.org/content/10.6...
biorxiv.org
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Susan Ajith @suzibot.bsky.social · 09/02/2026
🧨 Preprint alert Is it easier to find a ball than a shoe? The answer lies in how variable we think these objects are in the real-world. www.biorxiv.org/content/10.6... w/ the amazing @dkaiserlab.bsky.social & @luchunyeh.bsky.social 🦄 🧵1/8
biorxiv.org
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Cliona O'Doherty @clionaod.bsky.social · 02/02/2026
1/7 Can infants recognise the world around them? 👶🧠 As part of the FOUNDCOG project, we scanned 134 awake infants using fMRI. Published today in Nature Neuroscience, our research reveals 2-month-old infants already possess complex visual representations in VVC that align with DNNs.
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Kelsey Han @chihyehan.bsky.social · 30/01/2026
Human visual cortex representations may be much higher-dimensional than earlier work suggested, but are these higher dimensions of cortical activity actually relevant to behavior? Our new paper tackles this by studying how different people experience the same movies. 🧵 www.cell.com/current-biol...
cell.com
High-dimensional structure underlying individual differences in naturalistic visual experience
Han and Bonner reveal that individual visual experience arises from high-dimensional neural geometry distributed across multiple representational scales. By characterizing the full dimensional spectru...
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David Bau @davidbau.bsky.social · 26/01/2026
What should academics be doing right now? I have been writing up some thoughts on what the research says about effective action, and what universities specifically can do. davidbau.github.io/poetsandnurs... It's on GitHub. Suggestions and pull requests welcome. github.com/davidbau/poe...
Federal agents with weapons drawn, moments before murdering American citizens on the streets of Minneapolis at the dawn of 2026.
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Giacomo Aldegheri @gialdegheri.bsky.social · 16/01/2026
Now out in @cp-trendscognsci.bsky.social: our short response to @neurosteven.bsky.social & Edward de Haan's recent paper on the binding problem. We argue that the binding problem arises because of tradeoffs faced by any information processing system, including the brain and DNNs. shorturl.at/RGXzt
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Russell Epstein @russellepstein.bsky.social · 07/01/2026
Our new paper in @sfnjournals.bsky.social shows different neural systems for integrating views into places--PPA integrates views *of* a location (e.g., views of a landmark), while RSC integrates views *from* a location (e.g., views of a panorama). Work by the bluesky-less Linfeng Tony Han.
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Davide Cortinovis @davidecortinovis.bsky.social · 21/12/2025
Now in press at Nature Communications! www.nature.com/articles/s41... Check it out if you are interested in category selectivity, the organization of visual cortex, and topographic models!
nature.com
Investigating action topography in visual cortex and deep artificial neural networks - Nature Communications
This study shows that interaction with objects is an important dimension that shapes the way object categories such as hands and tools are organized in visual cortex and shows that artificial neural n...
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Mick Bonner @mickbonner.bsky.social · 11/12/2025
Dimensionality reduction may be the wrong approach to understanding neural representations. Our new paper shows that across human visual cortex, dimensionality is unbounded and scales with dataset size—we show this across nearly four orders of magnitude. journals.plos.org/ploscompbiol...
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Sander van Bree @sandervanbree.bsky.social · 02/12/2025
New Correspondence with @davidpoeppel.bsky.social in Nat Rev Neurosci. www.nature.com/articles/s41... Here, we critique a recent paper by Rosas et al. We argue that "Bottom-up" and "Top-down" neuroscience have various meanings in the literature. PDF: rdcu.be/eSKYI
nature.com
Top-down and bottom-up neuroscience as collections of practices - Nature Reviews Neuroscience
Nature Reviews Neuroscience - Top-down and bottom-up neuroscience as collections of practices
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Fernanda Ribeiro @felenitaribeiro.bsky.social · 01/12/2025
Investigating individual-specific topographic organization has traditionally been a resource-intensive and time-consuming process. But what if we could map visual cortex organization in thousands of brains? Here we offer the community with a toolbox that can do just that! tinyurl.com/deepretinotopy
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Joao Barbosa @jbarbosa.org · 25/11/2025
Y’all are reading this paper in the wrong way. We love to trash dominant hypothesis, but we need to look for evidence against the manifold hypothesis elsewhere: This elegant work doesn't show neural dynamics are high D, nor that we should stop using PCA It’s quite the opposite! (thread)
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J. Brendan Ritchie @jbrendanritchie.bsky.social · 29/08/2025
Our target discussion article out in Cognitive Neuroscience! It will be followed by peer commentary and our responses. If you would like to write a commentary, please reach out to the journal! 1/18 www.tandfonline.com/doi/full/10.... @cibaker.bsky.social @susanwardle.bsky.social
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Lenny van Dyck @levandyck.bsky.social · 11/08/2025
Really looking forward to #CCN2025! On Tuesday, I'm presenting new work with @kathadobs.bsky.social on segregated vs. integrated face & body processing in visual cortex 😊🧍🧠 Using DNNs & fMRI, we test competing hypotheses, finding both distinct & shared selectivity. Come by Poster A64 for more.
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Martin Hebart @martinhebart.bsky.social · 11/08/2025
Very much looking forward to #CCN2025! Would love to see you at our lab's talks and posters, and meet me at the panel discussion in the Algonauts session on Wednesday!
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Davide Cortinovis @davidecortinovis.bsky.social · 07/08/2025
New preprint out! We propose that action is a key dimension shaping the topographic organization of object categories in lateral occipitotemporal cortex (LOTC)—and test whether standard and topographic neural networks capture this pattern. A thread: www.biorxiv.org/content/10.1... 🧵 1/n
biorxiv.org
Investigating action topography in visual cortex and deep artificial neural networks
High-level visual cortex contains category-selective areas embedded within larger-scale topographic maps like animacy and real-world size. Here, we propose action as a key organizing factor shaping vi...
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Vivian Paulun @vivianpaulun.bsky.social · 01/08/2025
Super excited to share our new article: “Dissociable cortical regions represent things and stuff in the human brain” with @nancykanwisher.bsky.social, @rtpramod.bsky.social and @joshtenenbaum.bsky.social Video abstract: www.youtube.com/watch?v=B0XR... Paper: authors.elsevier.com/a/1lWxv3QW8S...
youtube.com
Things and Stuff: How the brain distinguishes oozing fluids from solid objects
YouTube video by McGovern Institute
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Yingtian (David) Tang @davidtyt.bsky.social · 30/07/2025
🧠 NEW PREPRINT Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective www.biorxiv.org/content/10.1...
biorxiv.org
Many-Two-One: Diverse Representations Across Visual Pathways Emerge from A Single Objective
How the human brain supports diverse behaviours has been debated for decades. The canonical view divides visual processing into distinct "what" and "where/how" streams – however, their origin and inde...
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Martin Hebart @martinhebart.bsky.social · 23/06/2025
What makes humans similar or different to AI? In a paper out in @natmachintell.nature.com led by @florianmahner.bsky.social & @lukasmut.bsky.social, w/ Umut Güclü, we took a deep look at the factors underlying their representational alignment, with surprising results. www.nature.com/articles/s42...
nature.com
Dimensions underlying the representational alignment of deep neural networks with humans - Nature Machine Intelligence
An interpretability framework that compares how humans and deep neural networks process images has been presented. Their findings reveal that, unlike humans, deep neural networks focus more on visual ...
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