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Found 16 papers, 10 papers with code
RLTHF: Targeted Human Feedback for LLM Alignment
Evaluations on HH-RLHF and TL;DR datasets show that RLTHF reaches full-human annotation-level alignment with only 6-7% of the human annotation effort.
CoRNStack: High-Quality Contrastive Data for Better Code Ranking
Effective code retrieval plays a crucial role in advancing code generation, bug fixing, and software maintenance, particularly as software systems increase in complexity.
System States Forecasting of Microservices with Dynamic Spatio-Temporal Data
This paper introduces STMformer, a model tailored for forecasting system states in microservices environments, capable of handling multi-node and multivariate time series.
FIRST: Faster Improved Listwise Reranking with Single Token Decoding
Further, we incorporate a learning-to-rank loss during training, prioritizing ranking accuracy for the more relevant passages.
Mitigate Target-level Insensitivity of Infrared Small Target Detection via Posterior Distribution Modeling
Infrared Small Target Detection (IRSTD) aims to segment small targets from infrared clutter background.
CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generation
We develop the CloudEval-YAML benchmark with practicality in mind: the dataset consists of hand-written problems with unit tests targeting practical scenarios.
Ranked #1 on
Benchmarking
on CloudEval-YAML
Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection
Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field.
ILNet: Low-level Matters for Salient Infrared Small Target Detection
Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability.
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference
We study a normalizing flow in the latent space of a top-down generator model, in which the normalizing flow model plays the role of the informative prior model of the generator.
Smart Meters Integration in Distribution System State Estimation with Collaborative Filtering and Deep Gaussian Process
The problem of state estimations for electric distribution system is considered.
Sound2Synth: Interpreting Sound via FM Synthesizer Parameters Estimation
Synthesizer is a type of electronic musical instrument that is now widely used in modern music production and sound design.
SAS: Self-Augmentation Strategy for Language Model Pre-training
In this paper, we propose a self-augmentation strategy (SAS) where a single network is utilized for both regular pre-training and contextualized data augmentation for the training in later epochs.
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
We propose a generative model of unordered point sets, such as point clouds, in the form of an energy-based model, where the energy function is parameterized by an input-permutation-invariant bottom-up neural network.
Energy-Based Continuous Inverse Optimal Control
The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations.
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context.
Generative Hierarchical Learning of Sparse FRAME Models
This paper proposes a method for generative learning of hierarchical random field models.
