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Found 14 papers, 7 papers with code
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments.
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
The ability for a machine learning model to cope with differences in training and deployment conditions--e. g. in the presence of distribution shift or the generalization to new classes altogether--is crucial for real-world use cases.
In Search for a Generalizable Method for Source Free Domain Adaptation
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data.
GradMax: Growing Neural Networks using Gradient Information
The architecture and the parameters of neural networks are often optimized independently, which requires costly retraining of the parameters whenever the architecture is modified.
Halting Time is Predictable for Large Models: A Universality Property and Average-case Analysis
In fact, the halting time exhibits a universality property: it is independent of the probability distribution.
Fast Training of Sparse Graph Neural Networks on Dense Hardware
Graph neural networks have become increasingly popular in recent years due to their ability to naturally encode relational input data and their ability to scale to large graphs by operating on a sparse representation of graph adjacency matrices.
On the interplay between noise and curvature and its effect on optimization and generalization
The speed at which one can minimize an expected loss using stochastic methods depends on two properties: the curvature of the loss and the variance of the gradients.
Automatic differentiation in ML: Where we are and where we should be going
We review the current state of automatic differentiation (AD) for array programming in machine learning (ML), including the different approaches such as operator overloading (OO) and source transformation (ST) used for AD, graph-based intermediate representations for programs, and source languages.
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools.
Tangent: Automatic Differentiation Using Source Code Transformation in Python
Automatic differentiation (AD) is an essential primitive for machine learning programming systems.
Multiscale sequence modeling with a learned dictionary
We propose a generalization of neural network sequence models.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Blocks and Fuel: Frameworks for deep learning
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel.
Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks
One long-term goal of machine learning research is to produce methods that are applicable to reasoning and natural language, in particular building an intelligent dialogue agent.
