604,850 research outputs found
Scanner Invariant Representations for Diffusion MRI Harmonization
Purpose: In the present work we describe the correction of diffusion-weighted
MRI for site and scanner biases using a novel method based on invariant
representation.
Theory and Methods: Pooled imaging data from multiple sources are subject to
variation between the sources. Correcting for these biases has become very
important as imaging studies increase in size and multi-site cases become more
common. We propose learning an intermediate representation invariant to
site/protocol variables, a technique adapted from information theory-based
algorithmic fairness; by leveraging the data processing inequality, such a
representation can then be used to create an image reconstruction that is
uninformative of its original source, yet still faithful to underlying
structures. To implement this, we use a deep learning method based on
variational auto-encoders (VAE) to construct scanner invariant encodings of the
imaging data.
Results: To evaluate our method, we use training data from the 2018 MICCAI
Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our
proposed method shows improvements on independent test data relative to a
recently published baseline method on each subtask, mapping data from three
different scanning contexts to and from one separate target scanning context.
Conclusion: As imaging studies continue to grow, the use of pooled multi-site
imaging will similarly increase. Invariant representation presents a strong
candidate for the harmonization of these data
CausaLM: Causal Model Explanation Through Counterfactual Language Models
Understanding predictions made by deep neural networks is notoriously
difficult, but also crucial to their dissemination. As all ML-based methods,
they are as good as their training data, and can also capture unwanted biases.
While there are tools that can help understand whether such biases exist, they
do not distinguish between correlation and causation, and might be ill-suited
for text-based models and for reasoning about high level language concepts. A
key problem of estimating the causal effect of a concept of interest on a given
model is that this estimation requires the generation of counterfactual
examples, which is challenging with existing generation technology. To bridge
that gap, we propose CausaLM, a framework for producing causal model
explanations using counterfactual language representation models. Our approach
is based on fine-tuning of deep contextualized embedding models with auxiliary
adversarial tasks derived from the causal graph of the problem. Concretely, we
show that by carefully choosing auxiliary adversarial pre-training tasks,
language representation models such as BERT can effectively learn a
counterfactual representation for a given concept of interest, and be used to
estimate its true causal effect on model performance. A byproduct of our method
is a language representation model that is unaffected by the tested concept,
which can be useful in mitigating unwanted bias ingrained in the data.Comment: Our code and data are available at:
https://amirfeder.github.io/CausaLM/ Under review for the Computational
Linguistics journa
TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions
Although deep learning approaches have had tremendous success in image, video
and audio processing, computer vision, and speech recognition, their
applications to three-dimensional (3D) biomolecular structural data sets have
been hindered by the entangled geometric complexity and biological complexity.
We introduce topology, i.e., element specific persistent homology (ESPH), to
untangle geometric complexity and biological complexity. ESPH represents 3D
complex geometry by one-dimensional (1D) topological invariants and retains
crucial biological information via a multichannel image representation. It is
able to reveal hidden structure-function relationships in biomolecules. We
further integrate ESPH and convolutional neural networks to construct a
multichannel topological neural network (TopologyNet) for the predictions of
protein-ligand binding affinities and protein stability changes upon mutation.
To overcome the limitations to deep learning arising from small and noisy
training sets, we present a multitask topological convolutional neural network
(MT-TCNN). We demonstrate that the present TopologyNet architectures outperform
other state-of-the-art methods in the predictions of protein-ligand binding
affinities, globular protein mutation impacts, and membrane protein mutation
impacts.Comment: 20 pages, 8 figures, 5 table
Decay and interference effects in visuospatial short-term memory
No description supplie
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI. Since no single
planner can work well for all tasks and domains, portfolio-based techniques
have become increasingly popular in recent years. In particular, deep learning
emerges as a promising methodology for online planner selection. Owing to the
recent development of structural graph representations of planning tasks, we
propose a graph neural network (GNN) approach to selecting candidate planners.
GNNs are advantageous over a straightforward alternative, the convolutional
neural networks, in that they are invariant to node permutations and that they
incorporate node labels for better inference.
Additionally, for cost-optimal planning, we propose a two-stage adaptive
scheduling method to further improve the likelihood that a given task is solved
in time. The scheduler may switch at halftime to a different planner,
conditioned on the observed performance of the first one. Experimental results
validate the effectiveness of the proposed method against strong baselines,
both deep learning and non-deep learning based.
The code is available at \url{https://github.com/matenure/GNN_planner}.Comment: AAAI 2020. Code is released at
https://github.com/matenure/GNN_planner. Data set is released at
https://github.com/IBM/IPC-graph-dat
Learning Task Relatedness in Multi-Task Learning for Images in Context
Multimedia applications often require concurrent solutions to multiple tasks.
These tasks hold clues to each-others solutions, however as these relations can
be complex this remains a rarely utilized property. When task relations are
explicitly defined based on domain knowledge multi-task learning (MTL) offers
such concurrent solutions, while exploiting relatedness between multiple tasks
performed over the same dataset. In most cases however, this relatedness is not
explicitly defined and the domain expert knowledge that defines it is not
available. To address this issue, we introduce Selective Sharing, a method that
learns the inter-task relatedness from secondary latent features while the
model trains. Using this insight, we can automatically group tasks and allow
them to share knowledge in a mutually beneficial way. We support our method
with experiments on 5 datasets in classification, regression, and ranking tasks
and compare to strong baselines and state-of-the-art approaches showing a
consistent improvement in terms of accuracy and parameter counts. In addition,
we perform an activation region analysis showing how Selective Sharing affects
the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster
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