2 citations · 2 across the 17 of their papers we have counts for
19 papers
High-Dimensional Simulation-Based Inference in Latent Spaces
Lars Kühmichel, Stefan T. Radev, Bhanu Prasanna Koppolu +3
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations…
Hierarchical Utility Calibration for Structured Multiclass Decisions
Futoshi Futami, Jerry Huang, Ichiro Takeuchi
In multiclass probabilistic prediction, Utility Calibration (UC), which focuses auditing on specified utilities, has recently received attention as a way to guarantee downstream de…
ARM: Attention with Routed-Memory for Learnable Sparse Control
Qiuhao Zeng, Jerry Huang, Peng Lu +7
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 computa…
FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering
Jijun Chi, Zhenghan Tai, Hanwei Wu +21
Financial question answering over U.S. Securities and Exchange Commission (SEC) filings requires retrieving and synthesizing heterogeneous evidence dispersed across long, standardi…
Posterior Refinement: Fast Language Generation via Any-Order Flow Maps
Manan Agarwal, Sheel Shah, Chanhyuk Lee +6
Non-autoregressive generation offers a powerful paradigm for iterative refinement, allowing models to recursively critique, erase and regenerate arbitrary subsets of tokens. Howeve…
BayesFlow 2: Multi-Backend Amortized Bayesian Inference in Python
Lars Kühmichel, Jerry M. Huang, Valentin Pratz +11
Modern Bayesian inference involves a mixture of computational methods for estimating, validating, and drawing conclusions from probabilistic models as part of principled workflows.…
CogFormer: Learn All Your Models Once
Jerry M. Huang, Lukas Schumacher, Niek Stevenson +1
Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fit complex models that were previously…
Flow Map Language Models: One-step Language Modeling via Continuous Denoising
Chanhyuk Lee, Jaehoon Yoo, Manan Agarwal +6
Language models based on discrete diffusion have attracted widespread interest for their potential to provide faster generation than autoregressive models. Despite their promise, t…
Investigating the Multilingual Calibration Effects of Language Model Instruction-Tuning
Jerry Huang, Peng Lu, Qiuhao Zeng +5
Ensuring that deep learning models are well-calibrated in terms of their predictive uncertainty is essential in maintaining their trustworthiness and reliability, yet despite incre…
Beyond Hard Writes and Rigid Preservation: Soft Recursive Least-Squares for Lifelong LLM Editing
Xinyu Wang, Sicheng Lyu, Yu Gu +4
Model editing updates a pre-trained LLM with new facts or rules without retraining while preserving unrelated behavior. In real deployment, edits arrive as long streams, creating a…
Sublinear iterations can suffice even for DDPMs
Matthew S. Zhang, Stephen Huan, Jerry Huang +3
SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been fo…
VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Zhenghan Tai, Hanwei Wu, Qingchen Hu +24
Retrieval-Augmented Generation (RAG) is becoming increasingly essential for Question Answering (QA) in the financial sector, where accurate and contextually grounded insights from…
EvoEdit: Evolving Null-space Alignment for Robust and Efficient Knowledge Editing
Sicheng Lyu, Yu Gu, Xinyu Wang +5
Large language models (LLMs) require continual updates to rectify outdated or erroneous knowledge. Model editing has emerged as a compelling paradigm for introducing targeted modif…
Mamba Modulation: On the Length Generalization of Mamba
Peng Lu, Jerry Huang, Qiuhao Zeng +4
The quadratic complexity of the attention mechanism in Transformer models has motivated the development of alternative architectures with sub-quadratic scaling, such as state-space…
Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs
Behnoush Khavari, Mehran Shakerinava, Jayesh Khullar +4
Recent work has shown that LRNN models such as S4D, Mamba, and DeltaNet lack state-tracking capability due to either time-invariant transition matrices or restricted eigenvalue ran…
Calibrated Language Models and How to Find Them with Label Smoothing
Jerry Huang, Peng Lu, Qiuhao Zeng
Recent advances in natural language processing (NLP) have opened up greater opportunities to enable fine-tuned large language models (LLMs) to behave as more powerful interactive a…
PoTPTQ: A Two-step Power-of-Two Post-training for LLMs
Xinyu Wang, Vahid Partovi Nia, Peng Lu +4
Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, their deployment is challenging due to the su…
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application
Xueqing Peng, Lingfei Qian, Yan Wang +44
Real-world financial analysis involves information across multiple languages and modalities, from reports and news to scanned filings and meeting recordings. Yet most existing eval…
Predicting adaptively chosen observables in quantum systems
Jerry Huang, Laura Lewis, Hsin-Yuan Huang +1
Recent advances have demonstrated that measurements suffice to predict properties of arbitrarily large quantum many-body systems. However, these remarkabl…