A new causal deepset framework improves off-policy evaluation under complex interference.
problem Handling spatio-temporal interference in off-policy evaluation.
method Permutation invariance assumption and novel algorithms incorporating it.
result Significantly more precise estimations than existing methods.
New deep learning methods solve symmetric PDEs efficiently.
problem Solving nonlinear symmetric PDEs in high dimensions.
method Design of PointNet and DeepSet neural networks.
result DeepSet networks provide more accurate solutions and gradients.
New model closes gap in understanding equivariant set functions.
problem Understanding universality of equivariant set functions.
method Proves PointNet not equivariant universal and introduces PointNetST.
result PointNetST is the simplest permutation equivariant universal model.
Fishnets improve set and graph learning with scalable, robust aggregation.
problem Learning informative embeddings for sets and graphs with scalable and robust aggregation.
method Proposes Fishnets, a new aggregation strategy for set-based learning.
result Fishnets achieve state-of-the-art performance on graph datasets with fewer parameters and faster training.
Neural Local Wasserstein Regression models distribution-on-distribution regression with flexible, localized transport maps.
problem Estimating distribution-on-distribution regression with global optimal transport maps or linearization limitations.
method Proposes Neural Local Wasserstein Regression, a flexible nonparametric framework using locally defined transport maps in Wasserstein space.
result Demonstrates effective capture of nonlinear and high-dimensional distributional relationships.
Neural net reconstructs dark matter density from halo velocities.
problem Reconstructing local dark matter density from halo velocities.
method Hybrid architecture combining U-Net and DeepSets.
result Hybrid network recovers density amplitudes and phases better than U-Net.
New model learns multisets to predict containment and sizes of differences.
problem Learning permutation invariant representations for flexible containment.
method Formalize multisets, propose training on predicting symmetric difference sizes.
result Model outperforms DeepSets on predicting containment and sizes of symmetric differences.
We present a novel family of deep neural architectures, named partially exchangeable networks (PENs) that leverage probabilistic symmetries. By design, PENs are invariant to block-switch transformations, which characterize the partial exchangeability properties of conditionally Markovian processes. Moreover, we show th…
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data. Based on the recent DeepSets architecture proposed by Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an alternative…
The majority of contemporary object-tracking approaches do not model interactions between objects. This contrasts with the fact that objects' paths are not independent: a cyclist might abruptly deviate from a previously planned trajectory in order to avoid colliding with a car. Building upon HART, a neural class-agnost…
Meta-learning, or learning to learn, is a machine learning approach that utilizes prior learning experiences to expedite the learning process on unseen tasks. As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineer…
Study improves neural network generalization for invariant and equivariant data.
problem Developing a generalization theory for invariant and equivariant neural networks.
method Introducing quotient feature spaces to measure the effect of group actions on properties and proving a generalization error bound.
result The volume of quotient feature spaces can describe the generalization error and invariance/equivariance significantly improve the bound.
In this paper, we consider the problem of learning functions over sets, i.e., functions that are invariant to permutations of input set items. Recent approaches of pooling individual element embeddings can necessitate extremely large embedding sizes for challenging functions. We address this challenge by allowing stand…
META2 improves taxonomic classification and abundance estimation in metagenomics with deep learning and memory efficiency.
problem Memory constraints and inefficiencies in taxonomic classification and abundance estimation for metagenomics.
method Developed a novel memory-efficient read classification technique combining deep learning and locality-sensitive hashing, and formulated abundance estimation as a Multiple Instance Learning problem.
result Our approach outperforms conventional methods in both single-read taxonomic classification and abundance estimation, especially when memory is limited.
New basis for permutation equivariant layers reduces computation costs.
problem Efficiently computing permutation equivariant layers in neural networks.
method Generalized partition algebra basis with low-rank tensors.
result Low-rank tensors enable faster computation compared to orbit basis.