Publications (31)
Dynamic Frechet Regression with Feature Selection for Distributional Data
Kiran Adhikari, Amrutha Dinesh, Mathew Kuttolamadom +1
Many scientific and engineering applications generate responses that are not scalars or vectors, but statistical objects whose form evolves over an ordered index such as time, dept…
NVIDIA Nemotron 3: Efficient and Open Intelligence
NVIDIA, :, Aaron Blakeman +356
We introduce the Nemotron 3 family of models - Nano, Super, and Ultra. These models deliver strong agentic, reasoning, and conversational capabilities. The Nemotron 3 family uses a…
Nemotron 3 Ultra: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +571
We introduce Nemotron 3 Ultra, a 550 billion total and 55 billion active parameter Mixture-of-Experts Hybrid Mamba-Attention language model. We pre-trained Nemotron 3 Ultra on 20 t…
Beyond Static Bias: Adaptive Multi-Fidelity Bandits with Improving Proxies
Muyun Lu, Haoyang Hong, Huazheng Wang +1
As an extension of the classical multi-armed bandit problem, multi-fidelity multi-armed bandits (MF-MAB) enable individual arms to be evaluated using diverse feedback sources that…
Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
Syeda Nahida Akter, Shrimai Prabhumoye, Matvei Novikov +8
Large Language Models (LLMs) have shown strong reasoning capabilities, particularly when enhanced through Reinforcement Learning (RL). While prior work has successfully applied RL…
Acquiring Background Knowledge to Improve Moral Value Prediction
Ying Lin, Joe Hoover, Morteza Dehghani +2
In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis. This is a particularly challenging problem because moral values are…
Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Kaituo Zhang, Zhen Xiong, Mingyu Zhong +4
Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this conse…
GID: Graph-based Intrusion Detection on Massive Process Traces for Enterprise Security Systems
Boxiang Dong, Zhengzhang Chen, Hui Wang +5
Intrusion detection system (IDS) is an important part of enterprise security system architecture. In particular, anomaly-based IDS has been widely applied to detect abnormal proces…
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
Ying Lin, Han Wang, Jiangning Chen +5
The growing popularity of Virtual Assistants poses new challenges for Entity Resolution, the task of linking mentions in text to their referent entities in a knowledge base. Specif…
Tight error bounds for log-determinant cones without constraint qualifications
Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço +1
In this paper, without requiring any constraint qualifications, we establish tight error bounds for the log-determinant cone, which is the closure of the hypograph of the perspecti…
Collaborative Alerts Ranking for Anomaly Detection
Ying Lin, Zhengzhang Chen, Cheng Cao +5
Given a large number of low-level heterogeneous categorical alerts from an anomaly detection system, how to characterize complex relationships between different alerts, filter out…
Generalized power cones: optimal error bounds and automorphisms
Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço +1
Error bounds are a requisite for trusting or distrusting solutions in an informed way. Until recently, provable error bounds in the absence of constraint qualifications were unatta…
NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid Mamba-Transformer Reasoning Model
NVIDIA, :, Aarti Basant +214
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compar…
Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon Pretraining Dataset
Dan Su, Kezhi Kong, Ying Lin +6
Recent English Common Crawl datasets like FineWeb-Edu and DCLM achieved significant benchmark gains via aggressive model-based filtering, but at the cost of removing 90% of data. T…
A Grounded Unsupervised Universal Part-of-Speech Tagger for Low-Resource Languages
Ronald Cardenas, Ying Lin, Heng Ji +1
Unsupervised part of speech (POS) tagging is often framed as a clustering problem, but practical taggers need to \textit{ground} their clusters as well. Grounding generally require…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le +9
Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LL…
Ranking and Combining Latent Structured Predictive Scores without Labeled Data
Shiva Afshar, Yinghan Chen, Shizhong Han +1
Combining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the…
Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aaron Blakeman +311
We present Nemotron 3 Nano 30B-A3B, a Mixture-of-Experts hybrid Mamba-Transformer language model. Nemotron 3 Nano was pretrained on 25 trillion text tokens, including more than 3 t…
Adaptive perturbation adversarial training: based on reinforcement learning
Zhishen Nie, Ying Lin, Sp Ren +1
Adversarial training has become the primary method to defend against adversarial samples. However, it is hard to practically apply due to many shortcomings. One of the shortcomings…
Change-point detection in variance-covariance matrix
Ying Lin, Benjamin Poignard
We consider the joint estimation of change point locations and the sparsity pattern of the variance covariance matrix, which is assumed to evolve in a piecewise constant manner. By…
Llama-Nemotron: Efficient Reasoning Models
Akhiad Bercovich, Itay Levy, Izik Golan +132
We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an ope…
Change Point Detection in Precision Matrices with D-trace Loss
Ying Lin, Benjamin Poignard, Ting Kei Pong +1
We consider the problem of estimating a time-varying sparse precision matrix, which is assumed to evolve in a piecewise constant manner. Building upon the Group Fused LASSO and LAS…
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning
Tanapol Kosolwattana, Huazheng Wang, Raed Al Kontar +1
Online learning has demonstrated notable potential to dynamically allocate limited resources to monitor a large population of processes, effectively balancing the exploitation of p…
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
Yuxuan Li, Ying Lin, Chenang Liu
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics. Effective synthesis for expressions using given charac…
Online Modeling and Monitoring of Dependent Processes under Resource Constraints
Tanapol Kosolwattana, Huazheng Wang, Ying Lin
Adaptive monitoring of a large population of dynamic processes is critical for the timely detection of abnormal events under limited resources in many healthcare and engineering sy…
Cost-efficiency trade-offs of the human brain network revealed by a multiobjective evolutionary algorithm
Junji Ma, Jinbo Zhang, Ying Lin +1
It is widely believed that the formation of brain network structure is under the pressure of optimal trade-off between reducing wiring cost and promoting communication efficiency.…
COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation
Qingyun Wang, Manling Li, Xuan Wang +24
To combat COVID-19, both clinicians and scientists need to digest vast amounts of relevant biomedical knowledge in scientific literature to understand the disease mechanism and rel…
Decentralized Optimization with Topology-Independent Communication
Ying Lin, Yao Kuang, Ahmet Alacaoglu +1
Distributed optimization requires nodes to coordinate, yet full synchronization scales poorly. When nodes collaborate through pairwise regularizers, standard methods demand…
Nemotron-H: A Family of Accurate and Efficient Hybrid Mamba-Transformer Models
NVIDIA, :, Aaron Blakeman +198
As inference-time scaling becomes critical for enhanced reasoning capabilities, it is increasingly becoming important to build models that are efficient to infer. We introduce Nemo…
Permutation-preserving Functions and Neural Vecchia Covariance Kernels
Jian Cao, Nian Liu, Ying Lin
We introduce a novel framework for constructing scalable and flexible covariance kernels for Gaussian processes (GPs) by directly learning the covariance structure under a regressi…