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Found 10 papers, 5 papers with code
When Fuzzing Meets LLMs: Challenges and Opportunities
Fuzzing, a widely-used technique for bug detection, has seen advancements through Large Language Models (LLMs).
Infinite Photorealistic Worlds using Procedural Generation
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world.
Adaptive Task Offloading for Space Missions: A State-Graph-Based Approach
However, the overall delay is determined by both computation and transmission costs.
Network-Wide Task Offloading With LEO Satellites: A Computation and Transmission Fusion Approach
To address the network-wide offloading problem, we propose a metagraph-based computation and transmission fusion offloading scheme for multi-tier networks.
A Unified Framework of Surrogate Loss by Refactoring and Interpolation
We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surrogate losses.
Learning to Prove Theorems by Learning to Generate Theorems
We consider the task of automated theorem proving, a key AI task.
Ranked #2 on
Automated Theorem Proving
on Metamath set.mm
Speaker Naming in Movies
We propose a new model for speaker naming in movies that leverages visual, textual, and acoustic modalities in an unified optimization framework.
EnFuzz: From Ensemble Learning to Ensemble Fuzzing
For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications.
Software Engineering
Premise Selection for Theorem Proving by Deep Graph Embedding
We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture.
Ranked #1 on
Automated Theorem Proving
on HolStep (Unconditional)
LINE: Large-scale Information Network Embedding
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.
Ranked #5 on
Node Classification
on Wikipedia
