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Found 10 papers, 1 papers with code
Question Modifiers in Visual Question Answering
Visual Question Answering (VQA) is a challenge problem that can advance AI by integrating several important sub-disciplines including natural language understanding and computer vision.
Verifying Relational Explanations: A Probabilistic Approach
Explanations on relational data are hard to verify since the explanation structures are more complex (e. g. graphs).
On the verification of Embeddings using Hybrid Markov Logic
The standard approach to verify representations learned by Deep Neural Networks is to use them in specific tasks such as classification or regression, and measure their performance based on accuracy in such tasks.
ToolChain*: Efficient Action Space Navigation in Large Language Models with A* Search
It formulates the entire action space as a decision tree, where each node represents a possible API function call involved in a solution plan.
A Faster $k$-means++ Algorithm
We propose a new algorithm \textsc{FastKmeans++} that only takes in $\widetilde{O}(nd + nk^2)$ time, in total.
Optimal Bidding Strategy without Exploration in Real-time Bidding
Earlier works on optimal bidding strategy apply model-based batch reinforcement learning methods which can not generalize to unknown budget and time constraint.
Scalable Bid Landscape Forecasting in Real-time Bidding
The highest bidding advertiser wins but pays only the second-highest bid (known as the winning price).
Inferring Individual Level Causal Models from Graph-based Relational Time Series
We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes.
Fast Lifted MAP Inference via Partitioning
A key advantage of these lifted algorithms is that they have much smaller computational complexity than propositional algorithms when symmetries are present in the MLN and these symmetries can be detected using lifted inference rules.
An Integer Polynomial Programming Based Framework for Lifted MAP Inference
In this paper, we present a new approach for lifted MAP inference in Markov logic networks (MLNs).
