2 citations · 3 across the 16 of their papers we have counts for
16 papers
CD4LM: Consistency Distillation and aDaptive Decoding for Diffusion Language Models
Yihao Liang, Ze Wang, Hao Chen +7
Autoregressive large language models achieve strong results on many benchmarks, but decoding remains fundamentally latency-limited by sequential dependence on previously generated…
Knowledge Graphs are Implicit Reward Models: Path-Derived Signals Enable Compositional Reasoning
Yuval Kansal, Niraj K. Jha
Large language models have achieved near-expert performance in structured reasoning domains like mathematics and programming, yet their ability to perform compositional multi-hop r…
LinMU: Multimodal Understanding Made Linear
Hongjie Wang, Niraj K. Jha
Modern Vision-Language Models (VLMs) achieve impressive performance but are limited by the quadratic complexity of self-attention, which prevents their deployment on edge devices a…
A Vision Toward Energy-Efficient Domain-Specific Artificial Intelligence Models and Agents
Abhijit Chatterjee, Niraj K. Jha, Jonathan D. Cohen +6
The field of artificial intelligence (AI) has taken a tight hold on broad aspects of society, industry, business, and governance in ways that dictate the prosperity and might of th…
Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Need
Bhishma Dedhia, Yuval Kansal, Niraj K. Jha
Language models traditionally used for cross-domain generalization have recently demonstrated task-specific reasoning. However, their top-down training approach on general corpora…
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Chang Yue, Niraj K. Jha
Differentiable logic networks (DLNs) have shown promising results in tabular domains by combining accuracy, interpretability, and computational efficiency. In this work, we apply D…
Learning Interpretable Differentiable Logic Networks for Tabular Regression
Chang Yue, Niraj K. Jha
Neural networks (NNs) achieve outstanding performance in many domains; however, their decision processes are often opaque and their inference can be computationally expensive in re…
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks
Bhishma Dedhia, David Bourgin, Krishna Kumar Singh +5
Diffusion Transformers (DiTs) can generate short photorealistic videos, yet directly training and sampling longer videos with full attention across the video remains computationall…
COMFORT: A Continual Fine-Tuning Framework for Foundation Models Targeted at Consumer Healthcare
Chia-Hao Li, Niraj K. Jha
Wearable medical sensors (WMSs) are revolutionizing smart healthcare by enabling continuous, real-time monitoring of user physiological signals, especially in the field of consumer…
Learning Interpretable Differentiable Logic Networks
Chang Yue, Niraj K. Jha
The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationshi…
CONFINE: Conformal Prediction for Interpretable Neural Networks
Linhui Huang, Sayeri Lala, Niraj K. Jha
Deep neural networks exhibit remarkable performance, yet their black-box nature limits their utility in fields like healthcare where interpretability is crucial. Existing explainab…
Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models
Hongjie Wang, Difan Liu, Yan Kang +4
Diffusion Models (DMs) have exhibited superior performance in generating high-quality and diverse images. However, this exceptional performance comes at the cost of expensive archi…
PAGE: Domain-Incremental Adaptation with Past-Agnostic Generative Replay for Smart Healthcare
Chia-Hao Li, Niraj K. Jha
We propose PAGE, a domain-incremental adaptation strategy with past-agnostic generative replay for smart healthcare. PAGE enables generative replay without the aid of any preserved…
Neural Slot Interpreters: Grounding Object Semantics in Emergent Slot Representations
Bhishma Dedhia, Niraj K. Jha
Several accounts of human cognition posit that our intelligence is rooted in our ability to form abstract composable concepts, ground them in our environment, and reason over these…
TAD-SIE: Sample Size Estimation for Clinical Randomized Controlled Trials using a Trend-Adaptive Design with a Synthetic-Intervention-Based Estimator
Sayeri Lala, Niraj K. Jha
Phase-3 clinical trials provide the highest level of evidence on drug safety and effectiveness needed for market approval by implementing large randomized controlled trials (RCTs).…
SECRETS: Subject-Efficient Clinical Randomized Controlled Trials using Synthetic Intervention
Sayeri Lala, Niraj K. Jha
The randomized controlled trial (RCT) is the gold standard for estimating the average treatment effect (ATE) of a medical intervention but requires 100s-1000s of subjects, making i…