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Rensselaer Polytechnic Institute

Academic institutionnorthamerica · us
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Research library422linked papers
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Selected work

Representative Papers

Tracking and Predicting Evolution of Social Communities

Oct 01, 20112011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing

研究开发了一种算法框架来追踪和预测社交网络中社区的演变，通过分析社区早期特征预测其寿命。

34 citations1 influentialRead paper

Memorization to Generalization: Emergence of Diffusion Models from Associative Memory

May 27, 2025

This work investigates the memory–generalization phase transition in diffusion models under varying training data scales. We propose a *correlational memory* perspective: training corresponds to memory encoding, while generation implements memory retrieval. We establish, for the first time, a theoretical connection between diffusion models and Hopfield networks, deriving necessary and sufficient conditions for the emergence of *spurious attractors*—hallucinated states—at the critical memory load threshold. Leveraging energy landscape analysis, dynamical systems modeling, and empirical validation on DDPM and DDIM, we confirm the universality of this phenomenon. Results show that models operate dominantly in memory mode under small-data regimes, shift toward generalization with large-scale data, and exhibit spurious attractors in the critical regime—unifying explanations for memory overload and implicit manifold learning. This work provides a cross-disciplinary theoretical framework and falsifiable predictions for understanding the intrinsic mechanisms of diffusion models.

3 citationsRead paper

Pipeline Gradient-based Model Training on Analog In-memory Accelerators

Oct 19, 2024arXiv.org

This work addresses two key challenges in large-model training on analog in-memory computing (AIMC) accelerators: (1) restricted data parallelism due to inefficient weight replication, and (2) stale weights and analog-domain deviations caused by asynchronous pipelined gradient updates. To this end, we propose Analog-SGD-AP—an asynchronous pipelined gradient descent algorithm tailored for AIMC. We establish the first convergence theory for AIMC architectures that jointly models physical non-idealities (e.g., device noise, nonlinear conductance response, weight update latency) and asynchronous timing behavior, rigorously deriving upper bounds on clock cycles and sample complexity. Analog-SGD-AP breaks the traditional data-parallelism bottleneck, enabling scalable multi-chip collaborative training. Evaluated on real datasets, it achieves convergence comparable to digital pipelined training while significantly improving training throughput and hardware efficiency.

2 citationsRead paper

Amortized Bayesian Workflow

Sep 06, 2024

Bayesian inference often faces a trade-off between computational efficiency and posterior accuracy, especially across multiple datasets. This paper proposes an adaptive hybrid inference workflow that—uniquely—integrates amortized variational inference (AVI) with Markov chain Monte Carlo (MCMC) in a dynamically coordinated manner. Leveraging principled posterior diagnostics, it constructs a Pareto frontier to enable automatic, optimal switching between AVI and MCMC. Computational reuse and scheduling optimization further boost inference throughput. The method unifies generative neural network modeling, MCMC refinement, and verifiable diagnostic mechanisms. Evaluated on tens of thousands of real and synthetic datasets, it achieves a 3.2× average speedup over standalone AVI or MCMC baselines, while preserving posterior fidelity—reducing KL divergence by 47% and increasing effective sample size (ESS) by 2.8×. This work delivers a scalable, efficient, and trustworthy solution for large-scale Bayesian inference.

2 citationsRead paper
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