10 research outputs found
The Convolution Exponential and Generalized Sylvester Flows
This paper introduces a new method to build linear flows, by taking the
exponential of a linear transformation. This linear transformation does not
need to be invertible itself, and the exponential has the following desirable
properties: it is guaranteed to be invertible, its inverse is straightforward
to compute and the log Jacobian determinant is equal to the trace of the linear
transformation. An important insight is that the exponential can be computed
implicitly, which allows the use of convolutional layers. Using this insight,
we develop new invertible transformations named convolution exponentials and
graph convolution exponentials, which retain the equivariance of their
underlying transformations. In addition, we generalize Sylvester Flows and
propose Convolutional Sylvester Flows which are based on the generalization and
the convolution exponential as basis change. Empirically, we show that the
convolution exponential outperforms other linear transformations in generative
flows on CIFAR10 and the graph convolution exponential improves the performance
of graph normalizing flows. In addition, we show that Convolutional Sylvester
Flows improve performance over residual flows as a generative flow model
measured in log-likelihood
MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data
Few-shot relation classification seeks to classify incoming query instances
after meeting only few support instances. This ability is gained by training
with large amount of in-domain annotated data. In this paper, we tackle an even
harder problem by further limiting the amount of data available at training
time. We propose a few-shot learning framework for relation classification,
which is particularly powerful when the training data is very small. In this
framework, models not only strive to classify query instances, but also seek
underlying knowledge about the support instances to obtain better instance
representations. The framework also includes a method for aggregating
cross-domain knowledge into models by open-source task enrichment.
Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a
few-shot relation classification dataset in health domain with purposely small
training data and challenging relation classes. Experimental results
demonstrate that our framework brings performance gains for most underlying
classification models, outperforms the state-of-the-art results given small
training data, and achieves competitive results with sufficiently large
training data
Equivariant Diffusion for Molecule Generation in 3D
This work introduces a diffusion model for molecule generation in 3D that is
equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model
(EDM) learns to denoise a diffusion process with an equivariant network that
jointly operates on both continuous (atom coordinates) and categorical features
(atom types). In addition, we provide a probabilistic analysis which admits
likelihood computation of molecules using our model. Experimentally, the
proposed method significantly outperforms previous 3D molecular generative
methods regarding the quality of generated samples and efficiency at training
time
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological
processes at temporal and spatial scales that would be intractable at an
atomistic resolution. However, accurately learning a CG force field remains a
challenge. In this work, we leverage connections between score-based generative
models, force fields and molecular dynamics to learn a CG force field without
requiring any force inputs during training. Specifically, we train a diffusion
generative model on protein structures from molecular dynamics simulations, and
we show that its score function approximates a force field that can directly be
used to simulate CG molecular dynamics. While having a vastly simplified
training setup compared to previous work, we demonstrate that our approach
leads to improved performance across several small- to medium-sized protein
simulations, reproducing the CG equilibrium distribution, and preserving
dynamics of all-atom simulations such as protein folding events
E(n) Equivariant Normalizing Flows
This paper introduces a generative model equivariant to Euclidean symmetries:
E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the
discriminative E(n) graph neural networks and integrate them as a differential
equation to obtain an invertible equivariant function: a continuous-time
normalizing flow. We demonstrate that E-NFs considerably outperform baselines
and existing methods from the literature on particle systems such as DW4 and
LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our
knowledge, this is the first flow that jointly generates molecule features and
positions in 3D
Contribution of natural and economic capital to subjective well-being: Empirical evidence from a small-scale society in Kodagu (Karnataka), India
Subjective well-being is determined by several types of sources of satisfaction, defined as forms of capitals. Most of research has been focused on the links between economic capital and well-being, neglecting the contribution of other forms of capital as source of satisfaction. Here, we bring natural capital into the equation and explore the relations between economic and natural capital and subjective well-being. We approach well-being as a multidimensional concept and then focus on three of its dimensions: subsistence, security, and reproduction and care. Working with tribal communities from Kodagu (Karnataka, India), we found positive associations between economic and natural capital and subjective well-being. Nevertheless, the two types of capitals differed on their relative contribution to (a) overall subjective well-being and (b) the three selected dimensions. Natural capital can be more important than economic capital in fulfilling human well-being. Findings support ongoing calls for explicitly incorporating ecological assets and ecosystem services in the design of policies oriented to measure and improve well-being