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Found 18 papers, 9 papers with code
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases
Question answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing.
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection
The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner.
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient
Diffusion models (DMs) are a type of generative model that has a huge impact on image synthesis and beyond.
Distilling Autoregressive Models to Obtain High-Performance Non-Autoregressive Solvers for Vehicle Routing Problems with Faster Inference Speed
To the best of our knowledge, this study is first-of-its-kind to obtain NAR VRP solvers from AR ones through knowledge distillation.
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR).
An Efficient 1 Iteration Learning Algorithm for Gaussian Mixture Model And Gaussian Mixture Embedding For Neural Network
We propose an Gaussian Mixture Model (GMM) learning algorithm, based on our previous work of GMM expansion idea.
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem with broad real-world applications.
Youku-mPLUG: A 10 Million Large-scale Chinese Video-Language Dataset for Pre-training and Benchmarks
In addition, to facilitate a comprehensive evaluation of video-language models, we carefully build the largest human-annotated Chinese benchmarks covering three popular video-language tasks of cross-modal retrieval, video captioning, and video category classification.
Shape-Aware Fine-Grained Classification of Erythroid Cells
BMEC contains 5, 666 images of individual erythroid cells, each of which is extracted from the bone marrow erythroid cell smears and professionally annotated to one of the four types of erythroid cells.
Differentiable Fuzzy $\mathcal{ALC}$: A Neural-Symbolic Representation Language for Symbol Grounding
DF-$\mathcal{ALC}$ unifies the description logic $\mathcal{ALC}$ and neural models for symbol grounding; in particular, it infuses an $\mathcal{ALC}$ knowledge base into neural models through differentiable concept and role embeddings.
New Coresets for Projective Clustering and Applications
$(j, k)$-projective clustering is the natural generalization of the family of $k$-clustering and $j$-subspace clustering problems.
Incorporating Surprisingly Popular Algorithm and Euclidean Distance-based Adaptive Topology into PSO
While many Particle Swarm Optimization (PSO) algorithms only use fitness to assess the performance of particles, in this work, we adopt Surprisingly Popular Algorithm (SPA) as a complementary metric in addition to fitness.
Neural Architecture Search based on Cartesian Genetic Programming Coding Method
To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy.
A Quest for Structure: Jointly Learning the Graph Structure and Semi-Supervised Classification
As such, PG-learn is a carefully-designed hybrid of random and adaptive search.
Coresets for Gaussian Mixture Models of Any Shape
For example, for any input set $D$ whose coordinates are integers in $[-n^{100}, n^{100}]$ and any fixed $k, d\geq 1$, the coreset size is $(\log n)^{O(1)}/\varepsilon^2$, and can be computed in time near-linear in $n$, with high probability.
Coresets for Ordered Weighted Clustering
We design coresets for Ordered k-Median, a generalization of classical clustering problems such as k-Median and k-Center, that offers a more flexible data analysis, like easily combining multiple objectives (e. g., to increase fairness or for Pareto optimization).
Data Structures and Algorithms
Wasserstein Identity Testing
In this paper, we propose the Wasserstein identity testing problem (Identity Testing in Wasserstein distance).
