115 research outputs found
Coarse-Graining Auto-Encoders for Molecular Dynamics
Molecular dynamics simulations provide theoretical insight into the
microscopic behavior of materials in condensed phase and, as a predictive tool,
enable computational design of new compounds. However, because of the large
temporal and spatial scales involved in thermodynamic and kinetic phenomena in
materials, atomistic simulations are often computationally unfeasible.
Coarse-graining methods allow simulating larger systems, by reducing the
dimensionality of the simulation, and propagating longer timesteps, by
averaging out fast motions. Coarse-graining involves two coupled learning
problems; defining the mapping from an all-atom to a reduced representation,
and the parametrization of a Hamiltonian over coarse-grained coordinates.
Multiple statistical mechanics approaches have addressed the latter, but the
former is generally a hand-tuned process based on chemical intuition. Here we
present Autograin, an optimization framework based on auto-encoders to learn
both tasks simultaneously. Autograin is trained to learn the optimal mapping
between all-atom and reduced representation, using the reconstruction loss to
facilitate the learning of coarse-grained variables. In addition, a
force-matching method is applied to variationally determine the coarse-grained
potential energy function. This procedure is tested on a number of model
systems including single-molecule and bulk-phase periodic simulations.Comment: 8 pages, 6 figure
Defensive Dropout for Hardening Deep Neural Networks under Adversarial Attacks
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That
is, adversarial examples, obtained by adding delicately crafted distortions
onto original legal inputs, can mislead a DNN to classify them as any target
labels. This work provides a solution to hardening DNNs under adversarial
attacks through defensive dropout. Besides using dropout during training for
the best test accuracy, we propose to use dropout also at test time to achieve
strong defense effects. We consider the problem of building robust DNNs as an
attacker-defender two-player game, where the attacker and the defender know
each others' strategies and try to optimize their own strategies towards an
equilibrium. Based on the observations of the effect of test dropout rate on
test accuracy and attack success rate, we propose a defensive dropout algorithm
to determine an optimal test dropout rate given the neural network model and
the attacker's strategy for generating adversarial examples.We also investigate
the mechanism behind the outstanding defense effects achieved by the proposed
defensive dropout. Comparing with stochastic activation pruning (SAP), another
defense method through introducing randomness into the DNN model, we find that
our defensive dropout achieves much larger variances of the gradients, which is
the key for the improved defense effects (much lower attack success rate). For
example, our defensive dropout can reduce the attack success rate from 100% to
13.89% under the currently strongest attack i.e., C&W attack on MNIST dataset.Comment: Accepted as conference paper on ICCAD 201
Learning Pair Potentials using Differentiable Simulations
Learning pair interactions from experimental or simulation data is of great
interest for molecular simulations. We propose a general stochastic method for
learning pair interactions from data using differentiable simulations
(DiffSim). DiffSim defines a loss function based on structural observables,
such as the radial distribution function, through molecular dynamics (MD)
simulations. The interaction potentials are then learned directly by stochastic
gradient descent, using backpropagation to calculate the gradient of the
structural loss metric with respect to the interaction potential through the MD
simulation. This gradient-based method is flexible and can be configured to
simulate and optimize multiple systems simultaneously. For example, it is
possible to simultaneously learn potentials for different temperatures or for
different compositions. We demonstrate the approach by recovering simple pair
potentials, such as Lennard-Jones systems, from radial distribution functions.
We find that DiffSim can be used to probe a wider functional space of pair
potentials compared to traditional methods like Iterative Boltzmann Inversion.
We show that our methods can be used to simultaneously fit potentials for
simulations at different compositions and temperatures to improve the
transferability of the learned potentials.Comment: 12 pages, 10 figure
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Membership inference attacks (MIAs) against machine learning models can lead
to serious privacy risks for the training dataset used in the model training.
In this paper, we propose a novel and effective Neuron-Guided Defense method
named NeuGuard against membership inference attacks (MIAs). We identify a key
weakness in existing defense mechanisms against MIAs wherein they cannot
simultaneously defend against two commonly used neural network based MIAs,
indicating that these two attacks should be separately evaluated to assure the
defense effectiveness. We propose NeuGuard, a new defense approach that jointly
controls the output and inner neurons' activation with the object to guide the
model output of training set and testing set to have close distributions.
NeuGuard consists of class-wise variance minimization targeting restricting the
final output neurons and layer-wise balanced output control aiming to constrain
the inner neurons in each layer. We evaluate NeuGuard and compare it with
state-of-the-art defenses against two neural network based MIAs, five strongest
metric based MIAs including the newly proposed label-only MIA on three
benchmark datasets. Results show that NeuGuard outperforms the state-of-the-art
defenses by offering much improved utility-privacy trade-off, generality, and
overhead
Accelerating Diffusion Sampling with Classifier-based Feature Distillation
Although diffusion model has shown great potential for generating higher
quality images than GANs, slow sampling speed hinders its wide application in
practice. Progressive distillation is thus proposed for fast sampling by
progressively aligning output images of -step teacher sampler with
-step student sampler. In this paper, we argue that this
distillation-based accelerating method can be further improved, especially for
few-step samplers, with our proposed \textbf{C}lassifier-based \textbf{F}eature
\textbf{D}istillation (CFD). Instead of aligning output images, we distill
teacher's sharpened feature distribution into the student with a
dataset-independent classifier, making the student focus on those important
features to improve performance. We also introduce a dataset-oriented loss to
further optimize the model. Experiments on CIFAR-10 show the superiority of our
method in achieving high quality and fast sampling. Code will be released soon
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