conference stringclasses 3
values | year int32 2.02k 2.02k | paper_id int32 5.89k 80k | title stringlengths 12 188 | abstract stringlengths 1 4.65k | topics listlengths 1 20 | image_url stringlengths 54 89 |
|---|---|---|---|---|---|---|
NeurIPS | 2,023 | 71,191 | MarioGPT: Open-Ended Text2Level Generation through Large Language Models | Procedural Content Generation (PCG) is a technique to generate complex and diverse environments in an automated way. However, while generating content with PCG methods is often straightforward, generating meaningful content that reflects specific intentions and constraints remains challenging. Furthermore, many PCG al... | [
"Procedural Content Generation ",
"Large Language Models ",
"Game Development",
"Artificial Intelligence in Games",
"Text-to-Level Generation",
"Machine Learning Applications in Gaming"
] | |
ICML | 2,023 | 24,254 | Towards Better Graph Representation Learning with Parameterized Decomposition & Filtering | Proposing an effective and flexible matrix to represent a graph is a fundamental challenge that has been explored from multiple perspectives, e.g., filtering in Graph Fourier Transforms. In this work, we develop a novel and general framework which unifies many existing GNN models from the view of parameterized decompos... | [
"Graph Representation Learning",
"Graph Neural Networks",
"Spectral Graph Theory",
"Computational Efficiency"
] | |
ICLR | 2,022 | 6,409 | Graph-Guided Network for Irregularly Sampled Multivariate Time Series | In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sam... | [
"Graph Neural Networks",
"Time Series Analysis",
"Healthcare Analytics",
"Computational Biology",
"Climate Science",
"Data Science"
] | |
NeurIPS | 2,023 | 71,085 | Empowering Convolutional Neural Nets with MetaSin Activation | ReLU networks have remained the default choice for models in the area of image prediction despite their well-established spectral bias towards learning low frequencies faster, and consequently their difficulty of reproducing high frequency visual details. As an alternative, sin networks showed promising results in lear... | [
"Neural Networks",
"Computer Vision",
"Image Processing",
"Activation Functions"
] | |
ICML | 2,024 | 34,864 | Boximator: Generating Rich and Controllable Motions for Video Synthesis | Generating rich and controllable motion is a pivotal challenge in video synthesis. We proposeBoximator, a new approach for fine-grained motion control. Boximator introduces two constraint types:hard boxandsoft box. Users select objects in the conditional frame using hard boxes and then use either type of boxes to rough... | [
"Video Synthesis",
"Motion Control",
"Computer Vision",
"Deep Learning",
"Generative Models"
] |
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