85 citations · 113 across the 15 of their papers we have counts for
18 papers
Looking Into The Past: The Impact of Context on Detecting Deceptive Statements
Alex Clay, Ernesto Jimenez-Ruiz, Pranava Madhyastha
Deception detection typically considers messages in isolation. Previous works in claim verification and role detection have displayed that additional information such as gameplay h…
In-Context Learning Can Help Vision-Language Models Overcome Training Priors
Kun Wang, Xindi Wu, Sanghyuk Chun +2
Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence when input images violate those…
Depth as Time in One-Step Generative Models
Arnold Caleb Asiimwe, William Yang, Sanghyuk Chun +2
The recent wave of one-step generative models, which compress the multi-step trajectory of diffusion via either distillation or learned flow maps, has reached an inflection point w…
Personalized Generative Models for Contextual Debiasing
Xinran Liang, Esin Tureci, Prachi Sinha +3
Different visual patterns appear with different frequencies in the world: e.g., beach balls appear on sand more often than they do on a road. These statistics are reflected in visi…
Bias at the End of the Score
Salma Abdel Magid, Grace Guo, Esin Tureci +4
Reward models (RMs) are inherently non-neutral value functions designed and trained to encode specific objectives, such as human preferences or text-image alignment. RMs have becom…
Prioritize the Process, Not Just the Outcome: Rewarding Latent Thought Trajectories Improves Reasoning in Looped Language Models
Jonathan Williams, Esin Tureci
Looped Language Models (LoopLMs) perform multi-step latent reasoning prior to token generation and outperform conventional LLMs on reasoning benchmarks at smaller parameter budgets…
Beyond Objects: Contextual Synthetic Data Generation for Fine-Grained Classification
William Yang, Xindi Wu, Zhiwei Deng +2
Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating effective synthetic training data for classification remains challenging. Fine-tun…
Visual Compositional Tuning
Xindi Wu, Hee Seung Hwang, Polina Kirichenko +2
Visual instruction tuning (VIT) datasets have grown rapidly in scale, yet the informativeness of individual training samples has largely been overlooked. Recent dataset selection m…
Co-Designed Superconducting Architecture for Lattice Surgery of Surface Codes with Quantum Interface Routing Card
Charles Guinn, Samuel Stein, Esin Tureci +5
Facilitating the ability to achieve logical qubit error rates below physical qubit error rates, error correction is anticipated to play an important role in scaling quantum compute…
Muchisim: A Simulation Framework for Design Exploration of Multi-Chip Manycore Systems
Marcelo Orenes-Vera, Esin Tureci, Margaret Martonosi +1
The design space exploration of scaled-out manycores for communication-intensive applications (e.g., graph analytics and sparse linear algebra) is hampered due to either lack of sc…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Tascade: Hardware Support for Atomic-free, Asynchronous and Efficient Reduction Trees
Marcelo Orenes-Vera, Esin Tureci, David Wentzlaff +1
Graph search and sparse data-structure traversal workloads contain challenging irregular memory patterns on global data structures that need to be modified atomically. Distributed…
DCRA: A Distributed Chiplet-based Reconfigurable Architecture for Irregular Applications
Marcelo Orenes-Vera, Esin Tureci, Margaret Martonosi +1
In recent years, the growing demand to process large graphs and sparse datasets has led to increased research efforts to develop hardware- and software-based architectural solution…
Microarchitectures for Heterogeneous Superconducting Quantum Computers
Samuel Stein, Sara Sussman, Teague Tomesh +12
Noisy Intermediate-Scale Quantum Computing (NISQ) has dominated headlines in recent years, with the longer-term vision of Fault-Tolerant Quantum Computation (FTQC) offering signifi…
Massive Data-Centric Parallelism in the Chiplet Era
Marcelo Orenes-Vera, Esin Tureci, David Wentzlaff +1
Recent works have introduced task-based parallelization schemes to accelerate graph search and sparse data-structure traversal, where some solutions scale up to thousands of proces…
Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks
Fangjun Hu, Gerasimos Angelatos, Saeed A. Khan +6
The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limit…
Dalorex: A Data-Local Program Execution and Architecture for Memory-bound Applications
Marcelo Orenes-Vera, Esin Tureci, David Wentzlaff +1
Applications with low data reuse and frequent irregular memory accesses, such as graph or sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor cor…
The MosaicSim Simulator (Full Technical Report)
Opeoluwa Matthews, Aninda Manocha, Davide Giri +7
As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly turning to heterogeneous parallelism and domain-specific hardware-software co-designs. These tr…