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Found 15 papers, 1 papers with code
DDI-CoCo: A Dataset For Understanding The Effect Of Color Contrast In Machine-Assisted Skin Disease Detection
We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models.
CSDR-BERT: a pre-trained scientific dataset match model for Chinese Scientific Dataset Retrieval
As the number of open and shared scientific datasets on the Internet increases under the open science movement, efficiently retrieving these datasets is a crucial task in information retrieval (IR) research.
FE-TCM: Filter-Enhanced Transformer Click Model for Web Search
In this paper, We use Transformer as the backbone network of feature extraction, add filter layer innovatively, and propose a new Filter-Enhanced Transformer Click Model (FE-TCM) for web search.
On Human Visual Contrast Sensitivity and Machine Vision Robustness: A Comparative Study
It is well established in neuroscience that color vision plays an essential part in the human visual perception system.
No-Regret Learning in Two-Echelon Supply Chain with Unknown Demand Distribution
Supply chain management (SCM) has been recognized as an important discipline with applications to many industries, where the two-echelon stochastic inventory model, involving one downstream retailer and one upstream supplier, plays a fundamental role for developing firms' SCM strategies.
Whom to Test? Active Sampling Strategies for Managing COVID-19
This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers.
Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multi-Armed Bandit Approach
In this study, we take a design-science perspective to propose a recommendation framework that helps users to select healthcare interventions.
Reinforcement Learning for Dynamic Bidding in Truckload Markets: an Application to Large-Scale Fleet Management with Advance Commitments
Truckload brokerages, a $100 billion/year industry in the U. S., plays the critical role of matching shippers with carriers, often to move loads several days into the future.
Optimal Learning for Sequential Decision Making for Expensive Cost Functions with Stochastic Binary Feedbacks
We also show that the knowledge gradient policy is asymptotically optimal in an offline setting.
MOLTE: a Modular Optimal Learning Testing Environment
The Matlab-based simulator allows the comparison of a number of learning policies (represented as a series of . m modules) in the context of a wide range of problems (each represented in its own . m module) which makes it easy to add new algorithms and new test problems.
An optimal learning method for developing personalized treatment regimes
A treatment regime is a function that maps individual patient information to a recommended treatment, hence explicitly incorporating the heterogeneity in need for treatment across individuals.
Finite-time Analysis for the Knowledge-Gradient Policy
We consider sequential decision problems in which we adaptively choose one of finitely many alternatives and observe a stochastic reward.
Functional Frank-Wolfe Boosting for General Loss Functions
Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance.
The Knowledge Gradient with Logistic Belief Models for Binary Classification
We consider sequential decision making problems for binary classification scenario in which the learner takes an active role in repeatedly selecting samples from the action pool and receives the binary label of the selected alternatives.
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules.
