67 citations · 295 across the 63 of their papers we have counts for
69 papers
FasterRW: An Efficient Prior-Assisted Random Walk Solver for Thermal Analysis
Zixiao Wang, Tianshu Hou, Chenghan Wang +5
Thermal simulation is becoming increasingly critical in modern IC design and manufacturing. Feynman--Kac random walk methods can estimate localized temperatures without solving the…
Kernel-Based Steering of CLIP with Vision-Language Model Preferences
Sajjad Ghiasvand, Haniyeh Ehsani Oskouie, Sina Mansouri +3
Large vision-language models (VLMs) can judge visual similarity, but their judgments are not directly available as compact image embeddings for efficient comparison. We study how t…
Do Large Language Models Capture the Diversity in their Training Data?
Youqi Wu, Farzan Farnia
Large language models are trained to model conditional distributions over text, yet it remains inadequately understood whether they capture the full diversity of plausible outputs…
Imaginative Generative AI: Crossing the Entropy Wall into Worlds Beyond Imitation
Farzan Farnia, Hossein Goli, Amin Gohari
Generative AI models are primarily designed to imitate the data distribution, an objective that neither corrects diversity lost by a learned generator nor defines how generation sh…
Exploring More to Solve More: Boosting Diversity in Text Diffusion Models via Entropy-Based Guidance
Jingwei Zhang, Haoyu Lei, Zijin Feng +2
Although diffusion models have revolutionized continuous domains like image synthesis through high quality generations and controllable guidance mechanisms, bringing this controlla…
KODA: Contrastive Representation Comparison and Alignment for Vision-Language Foundation Models
Youqi Wu, Mohammad Jalali, Farzan Farnia
Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are typically compared through dow…
DAK-UCB: Diversity-Aware Prompt Routing for LLMs and Generative Models
Donya Jafari, Farzan Farnia
The expansion of generative AI and LLM services underscores the growing need for adaptive mechanisms to select an appropriate available model to respond to a user's prompts. Recent…
Mixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
Bahar Dibaei Nia, Farzan Farnia
Efficient selection among multiple generative models is increasingly important in modern generative AI, where sampling from suboptimal models is costly. This problem can be viewed…
Exposing Diversity Bias in Deep Generative Models: Statistical Origins and Correction of Diversity Error
Farzan Farnia, Mohammad Jalali, Azim Ospanov
Deep generative models have achieved great success in producing high-quality samples, making them a central tool across machine learning applications. Beyond sample quality, an imp…
PromptSplit: Revealing Prompt-Level Disagreement in Generative Models
Mehdi Lotfian, Mohammad Jalali, Farzan Farnia
Prompt-guided generative AI models have rapidly expanded across vision and language domains, producing realistic and diverse outputs from textual inputs. The growing variety of suc…
The Maximum von Neumann Entropy Principle: Theory and Applications in Machine Learning
Youqi Wu, Farzan Farnia
Von Neumann entropy (VNE) is a fundamental quantity in quantum information theory and has recently been adopted in machine learning as a spectral measure of diversity for kernel ma…
On the Fragility of AI-Based Channel Decoders under Small Channel Perturbations
Haoyu Lei, Mohammad Jalali, Chin Wa Lau +1
Recent advances in deep learning have led to AI-based error correction decoders that report empirical performance improvements over traditional belief-propagation (BP) decoding on…
MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
Matina Mahdizadeh Sani, Nima Jamali, Mohammad Jalali +1
Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their outputs often deviate from the character…
Syndrome-Flow Consistency Model Achieves One-step Denoising Error Correction Codes
Haoyu Lei, Chin Wa Lau, Kaiwen Zhou +2
Error Correction Codes (ECC) are fundamental to reliable digital communication, yet designing neural decoders that are both accurate and computationally efficient remains challengi…
miniF2F-Lean Revisited: Reviewing Limitations and Charting a Path Forward
Azim Ospanov, Farzan Farnia, Roozbeh Yousefzadeh
We perform a thorough analysis of the formal and informal statements in the miniF2F benchmark from the perspective of an AI system that is tasked to participate in a math Olympiad…
HERMES: Towards Efficient and Verifiable Mathematical Reasoning in LLMs
Azim Ospanov, Zijin Feng, Jiacheng Sun +3
Informal mathematics has been central to modern large language model (LLM) reasoning, offering flexibility and efficient construction of arguments. However, purely informal reasoni…
PermLLM: Learnable Channel Permutation for N:M Sparse Large Language Models
Lancheng Zou, Shuo Yin, Zehua Pei +3
Channel permutation is a powerful technique for enhancing the accuracy of N:M sparse models by reordering the channels of weight matrices to prioritize the retention of important w…
Kernel-based Unsupervised Embedding Alignment for Enhanced Visual Representation in Vision-language Models
Shizhan Gong, Yankai Jiang, Qi Dou +1
Vision-language models, such as CLIP, have achieved significant success in aligning visual and textual representations, becoming essential components of many multi-modal large lang…
SPARKE: Scalable Prompt-Aware Diversity and Novelty Guidance in Diffusion Models via RKE Score
Mohammad Jalali, Haoyu Lei, Amin Gohari +1
Diffusion models have demonstrated remarkable success in high-fidelity image synthesis and prompt-guided generative modeling. However, ensuring adequate diversity in generated samp…
When Kernels Multiply, Clusters Unify: Fusing Embeddings with the Kronecker Product
Youqi Wu, Jingwei Zhang, Farzan Farnia
State-of-the-art embeddings often capture distinct yet complementary discriminative features: For instance, one image embedding model may excel at distinguishing fine-grained textu…
Towards an Explainable Comparison and Alignment of Feature Embeddings
Mohammad Jalali, Bahar Dibaei Nia, Farzan Farnia
While several feature embedding models have been developed in the literature, comparisons of these embeddings have largely focused on their numerical performance in classification-…
DiffPattern-Flex: Efficient Layout Pattern Generation via Discrete Diffusion
Zixiao Wang, Wenqian Zhao, Yunheng Shen +4
Recent advancements in layout pattern generation have been dominated by deep generative models. However, relying solely on neural networks for legality guarantees raises concerns i…
PromptWise: Online Learning for Cost-Aware Prompt Assignment in Generative Models
Xiaoyan Hu, Lauren Pick, Ho-fung Leung +1
The rapid advancement of generative AI has provided users with a wide range of well-trained models to address diverse prompts. When selecting a model for a given prompt, users shou…
APOLLO: Automated LLM and Lean Collaboration for Advanced Formal Reasoning
Azim Ospanov, Farzan Farnia, Roozbeh Yousefzadeh
Formal reasoning and automated theorem proving constitute a challenging subfield of machine learning, in which machines are tasked with proving mathematical theorems using formal l…