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Charles X Ling

7 accepted papers

2026

Attention with Routed-Memory for Learnable Sparse Control

ICML 2026poster

Despite advances in long-context inference, large language models (LLMs) remain fundamentally limited by the key-value (KV) caching mechanisms that are necessary for stable computation. Management techniques, such as selective token eviction and pruning, have vastly mitigated the issues that have ar…

Cited by 0SourceScholar
2026

Discretized Density-Guided Source-Free Adaptation for Continuous Targets

ICML 2026spotlight

Source-Free Domain Adaptation (SFDA) enables model adaptation under distribution shifts without access to source data, providing a practical solution for privacy-sensitive applications and having shown substantial progress in classification. In contrast, regression involves ordered and continuous ta…

Cited by 0SourceScholar
2026

FUSE: Full‑spectrum Unlearnable Examples via Spectral Equalization

ICML 2026poster

Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that existing UEs exhibit a critical failure once low-pass filtering is applied, indicating that the effective perturbation s…

Cited by 0SourceScholar
2024

Generalizing across Temporal Domains with Koopman Operators

AAAI 2024technical

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have…

Cited by 6SourcePDFScholar
2024

Physics-Informed Neural Networks: Minimizing Residual Loss with Wide Networks and Effective Activations

IJCAI 2024poster

The residual loss in Physics-Informed Neural Networks (PINNs) alters the simple recursive relation of layers in a feed-forward neural network by applying a differential operator, resulting in a loss landscape that is inherently different from those of common supervised problems. Therefore, relying o…

2021

Aggregating From Multiple Target-Shifted Sources

ICML 2021spotlight

Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different sources based on their relations. In this paper, we analyzed the problem for aggregating source domains with different…

Cited by 43SourcePDFScholar
2018

Pelee: A Real-Time Object Detection System on Mobile Devices

NeurIPS 2018poster

An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and Mob…