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Found 21 papers, 8 papers with code
Uncertainty Estimation of Transformer Predictions for Misclassification Detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such as active learning, misclassification detection, adversarial attack detection, out-of-distribution detection, etc.
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapes
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
Towards Computationally Feasible Deep Active Learning
Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models.
Adversarial Attacks on Deep Models for Financial Transaction Records
In this work, we examine adversarial attacks on transaction records data and defences from these attacks.
PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3D
This dataset is used to learn a convolutional autoencoder for point clouds sampled from the pairs of 3D scans - CAD models.
SHARP 2020: The 1st Shape Recovery from Partial Textured 3D Scans Challenge Results
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
CoLES: Contrastive Learning for Event Sequences with Self-Supervision
We address the problem of self-supervised learning on discrete event sequences generated by real-world users.
Minimal Variance Sampling in Stochastic Gradient Boosting
Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees.
Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling
We study the problem of aggregation noisy labels.
Aggregation of pairwise comparisons with reduction of biases
Answers to pairwise tasks are known to be affected by the position of items on the screen, however, previous models for aggregation of pairwise comparisons do not focus on modeling such kind of biases.
A survey on Deep Learning Advances on Different 3D Data Representations
3D data is a valuable asset the computer vision filed as it provides rich information about the full geometry of sensed objects and scenes.
CatBoost: unbiased boosting with categorical features
This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit.
Riemannian Optimization for Skip-Gram Negative Sampling
Skip-Gram Negative Sampling (SGNS) word embedding model, well known by its implementation in "word2vec" software, is usually optimized by stochastic gradient descent.
User Model-Based Intent-Aware Metrics for Multilingual Search Evaluation
We develop a novel approach to build intent-aware user behavior models, which overcome these limitations and convert to quality metrics that better correlate with standard online metrics of user satisfaction.
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
This study introduces a novel feature selection approach CMICOT, which is a further evolution of filter methods with sequential forward selection (SFS) whose scoring functions are based on conditional mutual information (MI).
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
In this paper, we consider a non-convex loss-minimization problem of learning Supervised PageRank models, which can account for features of nodes and edges.
Prediction of Video Popularity in the Absence of Reliable Data from Video Hosting Services: Utility of Traces Left by Users on the Web
We investigate video popularity prediction based on features from three primary sources available for a typical operating company: first, the content hosting provider may deliver its data via its API, second, the operating company makes use of its own search and browsing logs, third, the company crawls information about embeds of a video and links to a video page from publicly available resources on the Web.
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item.
Lower Bounds for Multi-armed Bandit with Non-equivalent Multiple Plays
We study the stochastic multi-armed bandit problem with non-equivalent multiple plays where, at each step, an agent chooses not only a set of arms, but also their order, which influences reward distribution.
