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Found 20 papers, 10 papers with code
Recurrent Neural Networks for Polyphonic Sound Event Detection in Real Life Recordings
In this paper we present an approach to polyphonic sound event detection in real life recordings based on bi-directional long short term memory (BLSTM) recurrent neural networks (RNNs).
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure.
Convolutional Recurrent Neural Networks for Bird Audio Detection
Bird sounds possess distinctive spectral structure which may exhibit small shifts in spectrum depending on the bird species and environmental conditions.
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task.
Avoiding Discrimination through Causal Reasoning
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.
Learning Independent Causal Mechanisms
The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization.
Tempered Adversarial Networks
A possible explanation for training instabilities is the inherent imbalance between the networks: While the discriminator is trained directly on both real and fake samples, the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset.
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
We introduce a method which enables a recurrent dynamics model to be temporally abstract.
Generalization in anti-causal learning
Anti-causal models are used to drive this search, but a causal model is required for validation.
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
Learning explanations that are hard to vary
In this paper, we investigate the principle that `good explanations are hard to vary' in the context of deep learning.
A Seq2Seq approach to Symbolic Regression
Deep neural networks have proved to be powerful function approximators.
Learning to interpret trajectories
By learning to predict trajectories of dynamical systems, model-based methods can make extensive use of all observations from past experience.
Divide-and-Conquer Monte Carlo Tree Search
are constrained by an implicit sequential planning assumption: The order in which a plan is constructed is the same in which it is executed.
Neural Symbolic Regression that Scales
We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
Explanatory Learning: Beyond Empiricism in Neural Networks
We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e. g. explanations written in hieroglyphic -- by autonomously learning to interpret them.
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Discovering ordinary differential equations that govern time-series
Natural laws are often described through differential equations yet finding a differential equation that describes the governing law underlying observed data is a challenging and still mostly manual task.
GPT-4 Technical Report
We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs.
Ranked #1 on
Long-Context Understanding
on Ada-LEval (BestAnswer)
Predicting Ordinary Differential Equations with Transformers
We develop a transformer-based sequence-to-sequence model that recovers scalar ordinary differential equations (ODEs) in symbolic form from irregularly sampled and noisy observations of a single solution trajectory.
