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Found 12 papers, 7 papers with code
Shallow Parsing for Nepal Bhasa Complement Clauses
The practice is easy to duplicate for training a shallow parser for other endangered languages in general.
Large Language Model Assisted Optimal Bidding of BESS in FCAS Market: An AI-agent based Approach
Secondly, the erratic decisions of the DRL agent caused by imperfectly predicted market information increases the risk of profit loss.
Path Choice Matters for Clear Attribution in Path Methods
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust.
LowRankOcc: Tensor Decomposition and Low-Rank Recovery for Vision-based 3D Semantic Occupancy Prediction
In this paper we present a tensor decomposition and low-rank recovery approach (LowRankOcc) for vision-based 3D semantic occupancy prediction.
OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving
In this paper, we explore a new framework of learning a world model, OccWorld, in the 3D Occupancy space to simultaneously predict the movement of the ego car and the evolution of the surrounding scenes.
SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction
Our SelfOcc outperforms the previous best method SceneRF by 58. 7% using a single frame as input on SemanticKITTI and is the first self-supervised work that produces reasonable 3D occupancy for surround cameras on nuScenes.
Fast Shapley Value Estimation: A Unified Approach
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks.
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries.
Attributable Visual Similarity Learning
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images.
Ranked #3 on
Metric Learning
on CARS196
(using extra training data)
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Deep Relational Metric Learning
This paper presents a deep relational metric learning (DRML) framework for image clustering and retrieval.
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning
Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data.
