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Found 9 papers, 2 papers with code
GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation
Offline imitation learning (IL) refers to learning expert behavior solely from demonstrations, without any additional interaction with the environment.
Two Timin': Repairing Smart Contracts With A Two-Layered Approach
Due to the modern relevance of blockchain technology, smart contracts present both substantial risks and benefits.
Coarse-Tuning Models of Code with Reinforcement Learning Feedback
Our experiments show that RLCF raises the odds that an LLM-generated program compiles, is executable, and produces the right output on tests, often allowing LLMs to match the performance of 2x-8x larger LLMs.
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution
We introduce a new triplet-based adversarial loss function that exploits the information provided in the LR image by using it as a negative sample.
Classification of genetic variants using machine learning
Recent advances in genomic sequencing technology have resulted in an abundance of genome sequence data.
Data Quality Toolkit: Automatic assessment of data quality and remediation for machine learning datasets
We attempt to re-look at the data quality issues in the context of building a machine learning pipeline and build a tool that can detect, explain and remediate issues in the data, and systematically and automatically capture all the changes applied to the data.
AStarTwice at SemEval-2021 Task 5: Toxic Span Detection Using RoBERTa-CRF, Domain Specific Pre-Training and Self-Training
This paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection.
DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder
We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.
Fast 3D Pose Refinement with RGB Images
Pose estimation is a vital step in many robotics and perception tasks such as robotic manipulation, autonomous vehicle navigation, etc.
