activity
20192026
most citedDeep Polynomial Neural Networks

81 citations · 108 across the 29 of their papers we have counts for

collaborators

32 papers

cs.LG2026

Domain Generalization under Sampling Pattern Shifts in Irregular Time Series

Changhun Kim, Joohyung Lee, Kwanhyung Lee +3

Irregularly sampled multivariate time series (ISMTS) are prevalent in real-world applications, where both observation times and available measurements can vary substantially across…

cs.AI2026

ChronoFlow: Hierarchical Flow Matching for Irregular Time Series Generation

Changhun Kim, Sunguk Jang, Jeongjun Lee +5

Recent advances in generative modeling have substantially improved time series generation, yet most existing methods either assume a regular temporal grid or focus on feature dynam…

cs.LG2026

Why DDIM Hallucinates More Than DDPM: A Theoretical Analysis of Reverse Dynamics

Muhammad H. Ashiq, Samanyu Arora, Abhinav N. Harish +3

We theoretically study the hallucination phenomena in two canonical diffusion samplers: the stochastic Denoising Diffusion Probabilistic Model (DDPM) and the deterministic Denoisin…

cs.CV2026

Activation-Free Backbones for Image Recognition: Polynomial Alternatives within MetaFormer-Style Vision Models

Jeffrey Wang, Jonathan Gregory, Grigorios G. Chrysos

Modern vision backbones treat pointwise activations (e.g., ReLU, GELU) and exponential softmax as essential sources of nonlinearity, but we demonstrate they are not required within…

cs.LG2026

LJ-Bench: Ontology-Based Benchmark for U.S. Crime

Hung Yun Tseng, Wuzhen Li, Blerina Gkotse +1

The potential of Large Language Models (LLMs) to provide harmful information remains a significant concern due to the vast breadth of illegal queries they may encounter. Unfortunat…

cs.LG2026

Corrective Diffusion Language Models

Shuibai Zhang, Fred Zhangzhi Peng, Yiheng Zhang +2

While Diffusion Language Models (DLMs) are theoretically well-suited for iterative refinement due to their non-causal structure, they often fail to reliably revise incorrect tokens…