Search Results for author:
Found 24 papers, 6 papers with code
Learning Probabilistic Temporal Logic Specifications for Stochastic Systems
We demonstrate the effectiveness of our algorithm in two use cases: learning from policies induced by RL algorithms and learning from variants of a probabilistic model.
What is Formal Verification without Specifications? A Survey on mining LTL Specifications
Virtually all verification techniques using formal methods rely on the availability of a formal specification, which describes the design requirements precisely.
DPO Kernels: A Semantically-Aware, Kernel-Enhanced, and Divergence-Rich Paradigm for Direct Preference Optimization
The rapid rise of large language models (LLMs) has unlocked many applications but also underscores the challenge of aligning them with diverse values and preferences.
Nemotron-4 340B Technical Report
We release the Nemotron-4 340B model family, including Nemotron-4-340B-Base, Nemotron-4-340B-Instruct, and Nemotron-4-340B-Reward.
CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Automatically designing fast and space-efficient digital circuits is challenging because circuits are discrete, must exactly implement the desired logic, and are costly to simulate.
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models
In this work, we introduce NV-Embed, incorporating architectural designs, training procedures, and curated datasets to significantly enhance the performance of LLM as a versatile embedding model, while maintaining its simplicity and reproducibility.
ChatQA: Surpassing GPT-4 on Conversational QA and RAG
In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-augmented generation (RAG) and conversational question answering (QA).
Ranked #6 on
Question Answering
on TriviaQA
(using extra training data)
Synthesizing Efficiently Monitorable Formulas in Metric Temporal Logic
In runtime verification, manually formalizing a specification for monitoring system executions is a tedious and error-prone process.
GraPhSyM: Graph Physical Synthesis Model
Given a gate-level netlist of a circuit represented as a graph, GraPhSyM utilizes graph structure, connectivity, and electrical property features to predict the impact of physical synthesis transformations such as buffer insertion and gate sizing.
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams
We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals.
Learning Temporal Logic Properties: an Overview of Two Recent Methods
Learning linear temporal logic (LTL) formulas from examples labeled as positive or negative has found applications in inferring descriptions of system behavior.
Analyzing Robustness of Angluin's L* Algorithm in Presence of Noise
Angluin's L* algorithm learns the minimal (complete) deterministic finite automaton (DFA) of a regular language using membership and equivalence queries.
Learning Interpretable Temporal Properties from Positive Examples Only
To learn meaningful models from positive examples only, we design algorithms that rely on conciseness and language minimality of models as regularizers.
Specification sketching for Linear Temporal Logic
Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding of the given system.
PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning
Deep Convolutional RL agents trained on this environment produce prefix adder circuits that Pareto-dominate existing baselines with up to 16. 0% and 30. 2% lower area for the same delay in the 32b and 64b settings respectively.
Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logic
Linear temporal logic (LTL) is a specification language for finite sequences (called traces) widely used in program verification, motion planning in robotics, process mining, and many other areas.
Guiding Global Placement With Reinforcement Learning
In this work we augment state-of-the-art, force-based global placement solvers with a reinforcement learning agent trained to improve the final detail placed Half Perimeter Wire Length (HPWL).
Learning Linear Temporal Properties from Noisy Data: A MaxSAT Approach
Our first algorithm infers minimal LTL formulas by reducing the inference problem to a problem in maximum satisfiability and then using off-the-shelf MaxSAT solvers to find a solution.
Machine Learning Link Inference of Noisy Delay-coupled Networks with Opto-Electronic Experimental Tests
To achieve this, we first train a type of machine learning system known as reservoir computing to mimic the dynamics of the unknown network.
Property-Directed Verification of Recurrent Neural Networks
This paper presents a property-directed approach to verifying recurrent neural networks (RNNs).
Learning Interpretable Models in the Property Specification Language
In contrast to most of the recent work in this area, which focuses on descriptions expressed in Linear Temporal Logic (LTL), we develop a learning algorithm for formulas in the IEEE standard temporal logic PSL (Property Specification Language).
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
Our technique leverages the results of a machine learning process for short time prediction to achieve our goal.
Critical Switching in Globally Attractive Chimeras
We report on a new type of chimera state that attracts almost all initial conditions and exhibits power-law switching behavior in networks of coupled oscillators.
Disordered Systems and Neural Networks Dynamical Systems Adaptation and Self-Organizing Systems Chaotic Dynamics Pattern Formation and Solitons
Topological Control of Synchronization Patterns: Trading Symmetry for Stability
Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics.
Adaptation and Self-Organizing Systems Disordered Systems and Neural Networks Chaotic Dynamics Pattern Formation and Solitons
