לוח שנה משולב

From rough sketches that spark ideas to polished illustrations that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms.

I will present my work on bridging generative models and visual communication, focusing on three complementary domains: (1) algorithms for generating and understanding sketches, (2) systems that support exploratory visual creation beyond one-shot generation, and (3) methods for producing editable, parametric images for design applications.

These domains pose unique challenges: they are inherently data-scarce and rely on representations that go beyond pixel-based images commonly used in standard models. I will show how the rich priors of vision-language models can be leveraged to address these challenges through novel optimization objectives and regularization techniques that connect their learned features with the specialized representations required for visual communication.

Looking ahead, this research lays the foundation for general-purpose visual communication technologies: intelligent systems that collaborate with humans in visual domains, enhancing how we design, learn, and exchange knowledge.

Bio:

Yael Vinker is a Postdoctoral Associate at MIT CSAIL, working with Prof. Antonio Torralba. She received her Ph.D. in Computer Science from Tel Aviv University, advised by Profs. Daniel Cohen-Or and Ariel Shamir. Her research spans computer graphics, computer vision, and machine learning, with a focus on generative models for visual communication. Her work has been recognized with two Best Paper Awards (SIGGRAPH 2022, SIGGRAPH Asia 2023) and a Best Paper Honorable Mention (SIGGRAPH 2023). She was selected as an MIT EECS Rising Star (2024) and received the Blavatnik Prize for Outstanding Israeli Doctoral Students in Computer Science (2024) as well as the VATAT Ph.D. Fellowship.

To coexist with our resident microbiota we must possess the ability to sense them and adjust our behavior. While the intestine is known to transduce nutrient signals to the brain to guide appetite, the mechanisms by which the host responds in real time to resident gut microbes have remained undefined. We found that specific colonic neuropod cells detect ubiquitous microbial signatures and communicate directly with vagal neurons to regulate feeding behavior. This pathway operates independently of immune or metabolic responses and suggests the host possesses a dedicated sensory circuit to maintain equilibrium. We call this sense at the interface of the biota and the brain the neurobiotic sense.

In this talk, we will explore circuit analysis for interpreting neural network models. After some background on the paradigm and techniques of circuit analysis, I'll present two (and a half) research studies demonstrating the breadth of these interpretability methods.

We will explore how this paradigm can help gain scientific insights into how neural network models operate, exemplified in the first work ("Arithmetic without Algorithms", https://technion-cs-nlp.github.io/llm-arithmetic-heuristics) where we use circuit analysis to reveal how language models solve arithmetic prompts. We will also show that circuit analysis can reveal findings on neural network models and help fix existing problems in them --- specifically targeting the issue of poor performance of VLMs on visual tasks compared to equivalent textual tasks (done in the work "Same Task, Different Circuits", https://technion-cs-nlp.github.io/vlm-circuits-analysis). Lastly, if time permits, we will discuss some current directions for future and ongoing work, mainly on scaling circuit analysis to complex tasks.

Bio:

Yaniv Nikankin is a PhD student at the Technion, working with Yonatan Belinkov. His work focuses on interpretability of neural networks, with a recent focus on scaling to analysis of long-form complex tasks. He is particularly excited about cross-domain applications of interpretability in scientific fields, for goals such as better understanding of scientific foundation models such as pLMs. Yaniv is a recipient of the Israeli Higher Education (VATAT) fellowship.

From rough sketches that spark ideas to polished illustrations that explain complex concepts, visual communication is central to how humans think, create, and share knowledge. Yet despite major advances in generative AI, we are still far from models that can reason and communicate through visual forms.

I will present my work on bridging generative models and visual communication, focusing on three complementary domains: (1) algorithms for generating and understanding sketches, (2) systems that support exploratory visual creation beyond one-shot generation, and (3) methods for producing editable, parametric images for design applications.

These domains pose unique challenges: they are inherently data-scarce and rely on representations that go beyond pixel-based images commonly used in standard models. I will show how the rich priors of vision-language models can be leveraged to address these challenges through novel optimization objectives and regularization techniques that connect their learned features with the specialized representations required for visual communication.

Looking ahead, this research lays the foundation for general-purpose visual communication technologies: intelligent systems that collaborate with humans in visual domains, enhancing how we design, learn, and exchange knowledge.

Bio:

Yael Vinker is a Postdoctoral Associate at MIT CSAIL, working with Prof. Antonio Torralba. She received her Ph.D. in Computer Science from Tel Aviv University, advised by Profs. Daniel Cohen-Or and Ariel Shamir. Her research spans computer graphics, computer vision, and machine learning, with a focus on generative models for visual communication. Her work has been recognized with two Best Paper Awards (SIGGRAPH 2022, SIGGRAPH Asia 2023) and a Best Paper Honorable Mention (SIGGRAPH 2023). She was selected as an MIT EECS Rising Star (2024) and received the Blavatnik Prize for Outstanding Israeli Doctoral Students in Computer Science (2024) as well as the VATAT Ph.D. Fellowship.