Zhijie Deng

LogCD: Local-to-global Consistency Distillation for Few-step Image Generation CVPR 2026 Mantis: A Versatile Vision-Language-Action Model with Disentangled Visual Foresight CVPR 2026 TPS-Bench: Evaluating AI Agents’ Tool Planning & Scheduling Abilities in Compounding Tasks ACL 2026 3D-Properties: Identifying Challenges in DPO and Charting a Path Forward ICLR 2025 Advancing Text-to-3D Generation with Linearized Lookahead Variational Score Distillation ICCV 2025 Fast Quiet-STaR: Thinking Without Thought Tokens EMNLP 2025 MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection ICLR 2025 Meta-Unlearning on Diffusion Models: Preventing Relearning Unlearned Concepts ICCV 2025 Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads ICML 2025 SafeEraser: Enhancing Safety in Multimodal Large Language Models through Multimodal Machine Unlearning ACL 2025 SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation AAAI 2025 SIFT: Grounding LLM Reasoning in Contexts via Stickers EMNLP 2025 Unveiling Uncertainty: A Deep Dive into Calibration and Performance of Multimodal Large Language Models COLING 2025 AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models EMNLP 2024 Amortized Fourier Neural Operators NeurIPS 2024 BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference ICLR 2024 Bayesian Exploration of Pre-trained Models for Low-shot Image Classification CVPR 2024 CLLMs: Consistency Large Language Models ICML 2024 Efficient Detection of LLM-generated Texts with a Bayesian Surrogate Model ACL 2024 Improved Operator Learning by Orthogonal Attention ICML 2024 Lost in Translation: Latent Concept Misalignment in Text-to-Image Diffusion Models ECCV 2024 Online Speculative Decoding ICML 2024 SpikeZIP-TF: Conversion is All You Need for Transformer-based SNN ICML 2024 Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation ICCV 2023 Learning Sample Difficulty from Pre-trained Models for Reliable Prediction NeurIPS 2023 On Calibrating Diffusion Probabilistic Models NeurIPS 2023 Towards Accelerated Model Training via Bayesian Data Selection NeurIPS 2023 Accelerated Linearized Laplace Approximation for Bayesian Deep Learning NeurIPS 2022 BayesAdapter: Being Bayesian, Inexpensively and Reliably, via Bayesian Fine-tuning ACML 2022 Confidence-based Reliable Learning under Dual Noises NeurIPS 2022 Efficient Inference for Dynamic Flexible Interactions of Neural Populations JMLR 2022 Exploring Memorization in Adversarial Training ICLR 2022 NeuralEF: Deconstructing Kernels by Deep Neural Networks ICML 2022 Black-Box Detection of Backdoor Attacks With Limited Information and Data ICCV 2021 LiBRe: A Practical Bayesian Approach to Adversarial Detection CVPR 2021 Adversarial Distributional Training for Robust Deep Learning NeurIPS 2020 AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning NeurIPS 2020 Understanding and Exploring the Network with Stochastic Architectures NeurIPS 2020 Cluster Alignment With a Teacher for Unsupervised Domain Adaptation ICCV 2019 Structured Generative Adversarial Networks NeurIPS 2017