conference
stringclasses 3
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int32 2.02k
2.02k
| paper_id
int32 5.89k
80k
| title
stringlengths 12
188
| abstract
stringlengths 1
4.65k
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20
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89
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|---|---|---|---|---|---|---|
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 algorithms lack the ability to generate content in an open-ended manner. Recently, Large Language Models (LLMs) have shown to be incredibly effective in many diverse domains. These trained LLMs can be fine-tuned, re-using information and accelerating training for new tasks. Here, we introduce MarioGPT, a fine-tuned GPT2 model trained to generate tile-based game levels, in our case Super Mario Bros levels. MarioGPT can not only generate diverse levels, but can be text-prompted for controllable level generation, addressing one of the key challenges of current PCG techniques. As far as we know, MarioGPT is the first text-to-level model and combined with novelty search it enables the generation of diverse levels with varying play-style dynamics (i.e. player paths) and the open-ended discovery of an increasingly diverse range of content. Code available at https://github.com/shyamsn97/mario-gpt.
|
[
"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 decomposition and filtering, and show how it helps to enhance the flexibility of GNNs while alleviating the smoothness and amplification issues of existing models. Essentially, we show that the extensively studied spectral graph convolutions with learnable polynomial filters are constrained variants of this formulation, and releasing these constraints enables our model to express the desired decomposition and filtering simultaneously. Based on this generalized framework, we develop models that are simple in implementation but achieve significant improvements and computational efficiency on a variety of graph learning tasks. Code is available at https://github.com/qslim/PDF.
|
[
"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 sampled and multivariate time series while also learning the dynamics of sensors purely from observational data. RAINDROP represents every sample as a separate sensor graph and models time-varying dependencies between sensors with a novel message passing operator. It estimates the latent sensor graph structure and leverages the structure together with nearby observations to predict misaligned readouts. This model can be interpreted as a graph neural network that sends messages over graphs that are optimized for capturing time-varying dependencies among sensors. We use RAINDROP to classify time series and interpret temporal dynamics on three healthcare and human activity datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4% (absolute F1-score points), including techniques that deal with irregular sampling using fixed discretization and set functions. RAINDROP shows superiority in diverse setups, including challenging leave-sensor-out settings.
|
[
"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 learning implicit representations of visual data. However training these networks in practically relevant settings proved to be difficult, requiring careful initialization, dealing with issues due to inconsistent gradients, and a degeneracy in local minima. In this work, we instead propose replacing a baseline network’s existing activations with a novel ensemble function with trainable parameters. The proposed MetaSin activation can be trained reliably without requiring intricate initialization schemes, and results in consistently lower test loss compared to alternatives. We demonstrate our method in the areas of Monte-Carlo denoising and image resampling where we set new state-of-the-art through a knowledge distillation based training procedure. We present ablations on hyper-parameter settings, comparisons with alternative activation function formulations, and discuss the use of our method in other domains, such as image classification.
|
[
"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 roughly or rigorously define the object’s position, shape, or motion path in future frames. Boximator functions as a plug-in for existing video diffusion models. Its training process preserves the base model’s knowledge by freezing the original weights and training only the control module. To address training challenges, we introduce a novelself-trackingtechnique that greatly simplifies the learning of box-object correlations. Empirically, Boximator achieves state-of-the-art video quality (FVD) scores, improving on two base models, and further enhanced after incorporating box constraints. Its robust motion controllability is validated by drastic increases in the bounding box alignment metric. Human evaluation also shows that users favor Boximator generation results over the base model.
|
[
"Video Synthesis",
"Motion Control",
"Computer Vision",
"Deep Learning",
"Generative Models"
] |
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