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Found 25 papers, 11 papers with code
Theoretical Physics Benchmark (TPBench) -- a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics
We introduce a benchmark to evaluate the capability of AI to solve problems in theoretical physics, focusing on high-energy theory and cosmology.
Anomaly Detection of Tabular Data Using LLMs
Large language models (LLMs) have shown their potential in long-context understanding and mathematical reasoning.
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior
In addition, learning an anomaly detector with limited (or no) anomalies often yields poor prediction performance.
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Towards Fast Stochastic Sampling in Diffusion Generative Models
We propose Splitting Integrators for fast stochastic sampling in pre-trained diffusion models in augmented spaces.
Hybrid Modeling Design Patterns
In this paper, we provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach.
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data
In this work, we propose SWSA (Selection With Synthetic Anomalies): a general-purpose framework to select image-based anomaly detectors without labeled validation data.
Efficient Integrators for Diffusion Generative Models
We propose two complementary frameworks for accelerating sample generation in pre-trained models: Conjugate Integrators and Splitting Integrators.
LoRA ensembles for large language model fine-tuning
To address these issues, we propose an ensemble approach using Low-Rank Adapters (LoRA), a parameter-efficient fine-tuning technique.
Deep Anomaly Detection on Tennessee Eastman Process Data
Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.
Deep Anomaly Detection under Labeling Budget Constraints
Selecting informative data points for expert feedback can significantly improve the performance of anomaly detection (AD) in various contexts, such as medical diagnostics or fraud detection.
Zero-Shot Anomaly Detection via Batch Normalization
Anomaly detection (AD) plays a crucial role in many safety-critical application domains.
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Raising the Bar in Graph-level Anomaly Detection
Graph-level anomaly detection has become a critical topic in diverse areas, such as financial fraud detection and detecting anomalous activities in social networks.
Complex-Valued Autoencoders for Object Discovery
Object-centric representations form the basis of human perception, and enable us to reason about the world and to systematically generalize to new settings.
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
We propose a strategy for training an anomaly detector in the presence of unlabeled anomalies that is compatible with a broad class of models.
Detecting Anomalies within Time Series using Local Neural Transformations
We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology.
Modeling Irregular Time Series with Continuous Recurrent Units
Recurrent neural networks (RNNs) are a popular choice for modeling sequential data.
Switching Recurrent Kalman Networks
In addition, driving data can often be multimodal in distribution, meaning that there are distinct predictions that are likely, but averaging can hurt model performance.
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Data transformations (e. g. rotations, reflections, and cropping) play an important role in self-supervised learning.
Variational Dynamic Mixtures
Deep probabilistic time series forecasting models have become an integral part of machine learning.
Extending Machine Language Models toward Human-Level Language Understanding
We take language to be a part of a system for understanding and communicating about situations.
Word2net: Deep Representations of Language
Most embedding methods rely on a log-bilinear model to predict the occurrence of a word in a context of other words.
Structured Embedding Models for Grouped Data
Here we develop structured exponential family embeddings (S-EFE), a method for discovering embeddings that vary across related groups of data.
Dynamic Bernoulli Embeddings for Language Evolution
Word embeddings are a powerful approach for unsupervised analysis of language.
Edward: A library for probabilistic modeling, inference, and criticism
Probabilistic modeling is a powerful approach for analyzing empirical information.
