Generative Model

22 items tagged

Visual Neuroscience NeuroAI Generative Model

Binxu Wang, Carlos R. Ponce (2026). Neuronal Tuning Aligns Dynamically with Object- and Texture Manifolds across the Visual Hierarchy. Nature Neuroscience.

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Neuronal Tuning Aligns Dynamically with Object- and Texture Manifolds across the Visual Hierarchy

Diffusion Science of AI Generative Model

Binxu Wang, Emma Lucia Byrnes Finn, Bingbin Liu (2025). When Rule Learning Breaks: Diffusion Fails to Learn Parity of Many Bits. Workshop on Structured Probabilistic Inference & Generative Modeling, NeurIPS 2025 (oral).

Diffusion NeuroAI Generative Model Science of AI

Binxu Wang, Jiaqi Shang, Haim Sompolinsky (2024). Diverse capability and scaling of diffusion and auto-regressive models when learning abstract rules. The First Workshop on System-2 Reasoning at Scale, NeurIPS'24.

Diffusion NeuroAI Generative Model Science of AI

Binxu Wang, Jiaqi Shang, Haim Sompolinsky (2024). Do diffusion models generalize on abstract rules for reasoning?. 2024 Conference on Cognitive Computational Neuroscience.

Visual Neuroscience Geometry Generative Model Ventral Stream

Binxu Wang, Carlos R Ponce (2024). Neural Dynamics of Object Manifold Alignment in the Ventral Stream. bioRxiv.

Diffusion Geometry Generative Model Science of AI Theory

Binxu Wang, John J Vastola (2023). The hidden linear structure in score-based models and its application. NeurIPS 2023 Workshop on Diffusion Models (arXiv:2311.10892).

Thesis Visual Neuroscience Generative Model

Binxu Wang (2023). Charting the Landscape of Ventral Stream Neural Code on Generative Image Manifolds.

Charting the Landscape of Ventral Stream Neural Code on Generative Image Manifolds

Diffusion Geometry Generative Model Science of AI Theory

Binxu Wang, John J Vastola (2023). Diffusion models generate images like painters: an analytical theory of outline first, details later. arXiv preprint arXiv:2303.02490.

Visual Neuroscience Geometry Generative Model Ventral Stream

Binxu Wang, Carlos R Ponce (2022). Tuning landscapes of the ventral stream. Cell reports.

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Tuning landscapes of the ventral stream

Geometry Generative Model Optimization

Binxu Wang, Carlos R Ponce (2021). The Geometry of Deep Generative Image Models and its Applications. International Conference on Learning Representations, 2021.

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The Geometry of Deep Generative Image Models and its Applications

Jun 2026

Houston Computational Neuroscience Journal Club

Invited talk at the Houston Computational Neuroscience Journal Club

Visual Neuroscience NeuroAI

Apr 2026

University of Texas at Austin — Xuexin Wei Lab Meeting

Neuronal Tuning Landscape on Generative Image Manifolds

Visual Neuroscience NeuroAI

Mar 2026

Flatiron Institute / CUNY — M.S. in Cognitive Science Special Seminar

From Closed-Loop Vision to Creative Machines: Generative Models as Tools and Theories of Neural Representation and Creativity

Visual Neuroscience NeuroAI

Feb 2026

University of Minnesota — Department of Neuroscience Special Seminar

From Closed-Loop Vision to Creative Machines: Generative Models as Tools and Theories of Neural Representation and Creativity

Visual Neuroscience NeuroAI

Feb 2026

University of Cincinnati — Department of Biological Sciences Special Seminar

From Closed-Loop Vision to Creative Machines: Generative Models as Tools and Theories of Neural Representation and Creativity

Visual Neuroscience NeuroAI

Jan 2026

University of Oregon, Knight Campus — Department of Bioengineering Special Symposium

Towards Generative and Predictive AI as Computational Interfaces to the Brain

Visual Neuroscience NeuroAI

Dec 2025

University of Wisconsin–Madison — Department of Psychology Special Seminar

From Closed-Loop Vision to Creative Machines: Generative Models as Tools and Theories of Neural Representation and Creativity

Visual Neuroscience NeuroAI

Oct 2025

Cold Spring Harbor Laboratory — NeuroAI Seminar

Model-Optimized Stimuli for Comparing Brain-Alignment of Generative Models and Encoding Models

Visual Neuroscience NeuroAI

Note on Variants of Diffusion Scheduler, DDPM DDIM PNDM

Motivation Given the popularity and power of diffusion models, the theoretical formulation of these models are not in unison. Because multiple groups have derived these models from different background, there exist multiple formulations, SDE, ODE, Markov Chain, Non-markov chain etc.

tech note Algorithm ODE SDE Dynamic System Generative Model Diffusion model

January 16, 2023

Note on Equivalent Score Matching Objective

TOC {:toc} Motivation Consider a distribution $p(x)$, we could “convolve” it with a kernel $p(\tilde{x}\mid x)=q(\tilde{x}-x)$. The marginal distribution of $\tilde{x}$ is denoted as $p_\sigma(\tilde{x})$. We want to model the score of this convolved distribution and that of the original distribution $\nabla\log p_\sigma(\tilde{x})$ .

tech note Algorithm Machine Learning Statistical Learning Generative Model probability

June 20, 2022

Note on Diffusion Generative Models

TOC {:toc} Motivation Recently, a line of research emerged in generative image models, diffusion models, which showed a competitive performance with GAN [^1]. More recently, a larger scale version of it gave rise to the ground breaking model DALL-E 2 and its precursor GLIDE.

tech note Algorithm Machine Learning Statistical Learning Generative Model

May 9, 2022

Note on Generative Adversarial Network

Note on GAN Note with reference to the Youtube lecture series Hongyi Li. Architecture Developments Self Attention Used in Self-Attention GAN and BigGAN class Self_Attn(nn.Module): """ Self attention Layer""" def __init__(self,in_dim,activation): super(Self_Attn,self).__init__() self.chanel_in = in_dim self.activation = activation self.query_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) self.key_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim//8 , kernel_size= 1) self.value_conv = nn.Conv2d(in_channels = in_dim , out_channels = in_dim , kernel_size= 1) self.gamma = nn.Parameter(torch.zeros(1)) self.softmax = nn.Softmax(dim=-1) # def forward(self,x): """ inputs : x : input feature maps( B X C X W X H) returns : out : self attention value + input feature attention: B X N X N (N is Width*Height) """ m_batchsize,C,width ,height = x.size() proj_query = self.query_conv(x).view(m_batchsize,-1,width*height).permute(0,2,1) # B X CX(N) proj_key = self.key_conv(x).view(m_batchsize,-1,width*height) # B X C x (*W*H) energy = torch.bmm(proj_query,proj_key) # transpose check attention = self.softmax(energy) # BX (N) X (N) proj_value = self.value_conv(x).view(m_batchsize,-1,width*height) # B X C X N out = torch.bmm(proj_value,attention.permute(0,2,1) ) out = out.view(m_batchsize,C,width,height) out = self.gamma*out + x return out,attention Style GAN BigGAN Conditional GAN Text Conditioning Text is processed and combined with noise vector.

academic note generative model computer vision computer graphics machine learning deep learning

March 7, 2020

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