adbX/reproscreener_manual_evaluations huggingface dataset:
arXiv paper 1903.09668
Problem: While the representation properties of
DL have been well studied, uncertainty quantification remains challenging and largely unexplored.
Objective: The purpose of our paper is to show that training DL architectures with data augmentation leads to efficiency gains.
Research method: We use the theory of scale mixtures of normals to derive data augmentation strategies for deep learning. This allows variants of the expectation-maximization and MCMC algorithms to be brought to bear on these high dimensional nonlinear deep learning models. To demonstrate our methodology, we develop data augmentation algorithms for a variety of commonly used activation functions: logit, ReLU, leaky ReLU and SVM. Our methodology is compared to traditional stochastic gradient descent with back-propagation. Our optimization procedure leads to a version of iteratively re-weighted least squares and can be implemented at scale with accelerated linear algebra methods providing substantial improvement in speed. We illustrate our methodology on a number of standard datasets. Finally, we conclude with directions for future research.
arXiv paper 1904.10554
Problem: Model-free learning for multi-agent stochastic games is an active area of
research. Existing reinforcement learning algorithms, however, are often
restricted to zero-sum games, and are applicable only in small state-action
spaces or other simplified settings.
Objective: Here, we develop a new data efficient
Deep-Q-learning methodology for model-free learning of Nash equilibria for
general-sum stochastic games.
Research method: The algorithm uses a local linear-quadratic
expansion of the stochastic game, which leads to analytically solvable optimal
actions. The expansion is parametrized by deep neural networks to give it
sufficient flexibility to learn the environment without the need to experience
all state-action pairs. We study symmetry properties of the algorithm stemming
from label-invariant stochastic games and as a proof of concept, apply our
algorithm to learning optimal trading strategies in competitive electronic
markets.
arXiv paper 1909.00931
Problem: While BERT's performance improves by increasing its model
size, the required computational power is an obstacle preventing practical
applications from adopting the technology.
Objective: Herein, we propose to inject phrasal
paraphrase relations into BERT in order to generate suitable representations
for semantic equivalence assessment instead of increasing the model size.
Research method: Herein, we propose to inject phrasal
paraphrase relations into BERT in order to generate suitable representations
for semantic equivalence assessment instead of increasing the model size.
Experiments on standard natural language understanding tasks confirm that our
method effectively improves a smaller BERT model while maintaining the model
size.
Hypothesis: Herein, we propose to inject
arXiv paper 1911.03867
Problem: The absence of large quantities of representative data from current astronomical surveys motivates the development
Objective: The absence of large quantities of
representative data from current astronomical surveys motivates the development
of a robust forward-modeling approach using synthetic lensing images. Using a
mock sample of strong lenses created upon a state-of-the-art extragalactic
catalogs, we train a modular deep learning pipeline for uncertainty-quantified
detection and modeling with intermediate image processing components for
denoising and deblending the lensing systems.
Research method: Using a mock sample of strong lenses created upon a state-of-the-art extragalactic catalogs, we train a modular deep learning pipeline for uncertainty-quantified detection and modeling with intermediate image processing components for denoising and deblending the lensing systems. We demonstrate a high degree of interpretability and controlled systematics due to domain-specific task modules trained with different stages of synthetic image generation. For lens detection and modeling, we obtain semantically meaningful latent spaces that separate classes of strong lens images and yield uncertainty estimates that explain the origin of misclassified images and provide probabilistic predictions for the lens parameters. Validation of the inference pipeline has been carried out using images from the Subaru telescope's Hyper Suprime-Cam camera, and LSST DESC simulated DC2 sky survey catalogues.
arXiv paper 2002.05905
Problem: However, USB flash drives are also one of the most
common attack vectors used to gain unauthorized access to host devices. For
instance, it is possible to replace a USB drive so that when the USB key is
connected, it would install passwords stealing tools, root-kit software, and
other disrupting malware. In such a way, an attacker can steal sensitive
information via the USB-connected devices, as well as inject any kind of
malicious software into the host.
Objective: To thwart the above-cited raising threats, we propose MAGNETO
Prediction: MAGNETO can also identify the
specific USB Flash drive, with a minimum classification accuracy of 91.2%
arXiv paper 2004.05258
Problem: Analyzing a huge amount of malware is a major burden for security analysts. Since emerging malware is often a variant of existing malware, automatically classifying malware into known families greatly reduces a part of their burden.
Objective: In this paper, we conducted an exhaustive survey of deep learning models using 24 ImageNet pre-trained models and five fine-tuning parameters, totaling 120 combinations, on two platforms.
Experiment setup: In this paper, we conducted an exhaustive survey of deep learning models using 24 ImageNet pre-trained models and five fine-tuning parameters, totaling 120 combinations, on two platforms.
arXiv paper 2009.01947
Problem: We present combinatorial and parallelizable algorithms for maximization of a submodular function, not necessarily monotone, with respect to a size constraint.
Objective: In this
version, we propose a fixed and improved subroutine to add a set with high
average marginal gain, \threseq, which returns a solution in $O( \log(n) )$
adaptive rounds with high probability.
Experiment setup: In this version, we propose a fixed and improved subroutine to add a set with high average marginal gain, \threseq, which returns a solution in $O( \log(n) )$ adaptive rounds with high probability. Moreover, we provide two approximation algorithms. The first has approximation ratio
arXiv paper 2010.04855
Problem: We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves.
Objective: We prove uniform consistency with finite sample rates
Research method: We prove uniform consistency with finite sample rates via original analysis of generalized kernel ridge regression. We extend our main results to counterfactual distributions and to causal functions identified by front and back door criteria.
arXiv paper 2011.11576
Problem: We propose the use of a conjecturing machine that generates feature relationships in the form of bounds involving nonlinear terms for numerical features and boolean expressions for categorical features.
Research method: We then compare the method to a previously-proposed framework for symbolic regression and demonstrate that it can also be used to recover equations that are satisfied among features in a dataset. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in medical literature.
arXiv paper 2012.09302
Problem: Neural backdoors represent one primary threat to the security of deep learning systems. The intensive research has produced a plethora of backdoor attacks/defenses, resulting in a constant arms race.
Objective: To bridge this gap, we design and implement TROJANZOO, the first open-source
platform for evaluating neural backdoor attacks/defenses in a unified,
holistic, and practical manner.
Research method: Leveraging TROJANZOO, we conduct a systematic study on the existing attacks/defenses, unveiling their complex design spectrum: both manifest intricate trade-offs among multiple desiderata (e.g., the effectiveness, evasiveness, and transferability of attacks). We further explore improving the existing attacks/defenses, leading to a number of interesting findings
Research questions: many critical questions remain under-explored: (i) what are the strengths and limitations of different attacks/defenses? (ii) what are the best practices to operate them? and (iii) how can the existing attacks/defenses be further improved?
Code availability: To bridge this gap, we design and implement TROJANZOO, the first open-source
arXiv paper 2101.07354
Problem: Random-walk based network embedding algorithms like DeepWalk and node2vec are widely used to obtain Euclidean representation of the nodes in a network prior to performing downstream inference tasks. However, despite their impressive empirical performance, there is a lack of theoretical results explaining their large-sample behavior.
Objective: In this paper, we study node2vec and DeepWalk through the perspective of matrix factorization. In particular, we analyze these algorithms in the setting of community detection for stochastic blockmodel graphs (and their degree-corrected variants).
Research method: By exploiting the row-wise uniform perturbation bound for leading singular vectors, we derive high-probability error bounds between the matrix factorization-based node2vec/DeepWalk embeddings and their true counterparts, uniformly over all node embeddings.
arXiv paper 2104.11893
Problem: However, it relies on disentangling information heavily from a local range (i.e., a node and its 1-hop neighbors), while the local information in many cases can be uneven and incomplete, hindering the interpretabiliy power and model performance of DisenGCN.
Objective: In this paper, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement.
Research method: GD-GCN performs a statistical mixture modeling to derive a factor-aware latent continuous space, and then constructs different structures w.r.t. different factors from the revealed space. In this way, the global factor-specific information can be efficiently and selectively encoded via a message passing along these built structures, strengthening the intra-factor consistency
arXiv paper 2104.12546
Problem: The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events.
Objective: The aim of this work is to investigate any possible relationships between air quality and confirmed cases of COVID-19 in Italian districts. Specifically, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants.
Hypothesis: Our analysis confirms a significant
association of some environmental parameters with the spread of the virus.
arXiv paper 2105.01937
Problem: Yet, multi-view algorithms strongly depend on camera parameters; particularly, the relative transformations between the cameras. Such a dependency becomes a hurdle once shifting to dynamic capture in uncontrolled settings.
Objective: We introduce FLEX (Free muLti-view rEconstruXion), an
end-to-end extrinsic parameter-free multi-view model. FLEX is extrinsic
parameter-free (dubbed ep-free) in the sense that it does not require extrinsic
camera parameters.
Dataset: Code, trained models, and other materials are available on our project page.
Code availability: Code, trained models, and other materials are available on our project page.
arXiv paper 2105.15197
Problem: Debiased machine learning is a meta algorithm based on bias correction and
sample splitting to calculate confidence intervals for functionals, i.e. scalar
summaries, of machine learning algorithms.
Objective: We provide a nonasymptotic debiased machine learning theorem that encompasses any global or local functional of any machine learning algorithm that satisfies a few simple, interpretable conditions.
Research method: Formally, we prove consistency, Gaussian approximation, and semiparametric efficiency by finite sample arguments.
arXiv paper 2106.01528
Problem: Controlled feature selection aims to discover the features a response depends on while limiting the false discovery rate (FDR) to a predefined level. Recently, multiple deep-learning-based methods have been proposed to perform controlled feature selection through the Model-X knockoff framework. We demonstrate, however, that these methods often fail to control the FDR for two reasons
Objective: We propose a new procedure called FlowSelect to
perform controlled feature selection that does not suffer from either of these
two problems.
Research questions: We demonstrate, however, that these methods often fail to control the FDR for two reasons. First, these methods often learn inaccurate models of features. Second, the "swap" property, which is required for knockoffs to be valid, is often not well enforced.
arXiv paper 2106.03157
Problem: Existing combinatorial search methods are often complex and require some level of expertise. This work introduces a simple and efficient deep learning method for solving combinatorial problems with a predefined goal, represented by Rubik's Cube.
Objective: This work introduces a simple and efficient deep learning
method for solving combinatorial problems with a predefined goal, represented
by Rubik's Cube. We demonstrate that, for such problems, training a deep neural network on random scrambles branching from the goal state is sufficient to achieve near-optimal solutions.
arXiv paper 2106.03725
Objective: The paper defines and studies manifold (M) convolutional filters and neural networks (NNs). \emph{Manifold} filters and MNNs are defined in terms of the Laplace-Beltrami operator exponential and are such that \emph{graph} (G) filters and neural networks (NNs) are recovered as discrete approximations when the manifold is sampled.
arXiv paper 2106.06927
Problem: Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors.
Objective: To address these limitations, we propose the use of adversarially robust representations as a perceptual primitive for feature inversion. We train an adversarially robust encoder to extract disentangled and perceptually-aligned image representations, making them easily invertible.
arXiv paper 2106.07704
Problem: Yet previous RL algorithms for text generation, such as policy gradient (on-policy RL) and Q-learning (off-policy RL), are often notoriously inefficient or unstable to train due to the large sequence space and the sparse reward received only at the end of sequences.
Objective: In this paper, we introduce a new RL formulation for text generation from the soft Q-learning (SQL) perspective. It enables us to draw from the latest RL advances, such as path consistency learning, to combine the best of on-/off-policy updates, and learn effectively from sparse reward.
arXiv paper 2106.10898
Problem: For collaborative filtering, the classical method is training the model offline, then perform the online testing, but this approach can no longer handle the dynamic changes in user preferences which is the so-called cold start. So how to effectively recommend items to users in the absence of effective information?
Objective: To address the aforementioned problems, a multi-armed bandit based collaborative filtering recommender system has been proposed, named BanditMF
Research questions: BanditMF is designed to address two challenges in the multi-armed bandits algorithm and collaborative filtering: (1) how to solve the cold start problem for collaborative filtering under the condition of scarcity of valid information, (2) how to solve the sub-optimal problem of bandit algorithms in strong social relations domains caused by independently estimating unknown parameters associated with each user and ignoring correlations between users.
arXiv paper 2107.01131
Problem: Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning. While featuring superior stability, these estimators crucially depend on costly large-batch training, and they sacrifice bound tightness for variance reduction.
Objective: To overcome these limitations, we revisit the mathematics of popular variational MI bounds from the lens of unnormalized statistical modeling and convex optimization.
arXiv paper 2108.09779
Problem: We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator.
Objective: We present a system for learning a challenging dexterous manipulation task involving moving a cube to an arbitrary 6-DoF pose with only 3-fingers trained with NVIDIA's IsaacGym simulator. We show empirical benefits, both in
simulation and sim-to-real transfer, of using keypoints as opposed to
position+quaternion representations for the object pose in 6-DoF for policy
observations and in reward calculation to train a model-free reinforcement
learning agent.
Code availability: With the aim of assisting further research in learning in-hand manipulation, we make the codebase of our system, along with trained checkpoints that come with billions of steps of experience available, at https://s2r2-ig.github.io
arXiv paper 2109.12784
Problem: Motivated by the problem of learning with small sample sizes, this paper shows how to incorporate into support-vector machines (SVMs) those properties that have made convolutional neural networks (CNNs) successful.
Objective: We address this lacuna and show that positive definiteness indeed holds \textit{with high probability} for kernels based on the maximum similarity in the small training sample set regime of interest, and that they do yield the best results in that regime. We also show how additional properties such as their ability to incorporate local features at multiple spatial scales, e.g., as done in CNNs through max pooling, and to provide the benefits of composition through the architecture of multiple layers, can also be embedded into SVMs.
Dataset: We verify through experiments on widely available image sets that the resulting SVMs do provide superior accuracy in comparison to well-established deep neural network benchmarks for small sample sizes.
Experiment setup: We
address this lacuna and show that positive definiteness indeed holds
\textit{with high probability} for kernels based on the maximum similarity in
the small training sample set regime of interest, and that they do yield the
best results in that regime. We also show how additional properties such as
their ability to incorporate local features at multiple spatial scales, e.g.,
as done in CNNs through max pooling, and to provide the benefits of composition
through the architecture of multiple layers, can also be embedded into SVMs. We
verify through experiments on widely available image sets that the resulting
SVMs do provide superior accuracy in comparison to well-established deep neural
network benchmarks for small sample sizes.
arXiv paper 2110.02343
Problem: Quantum machine learning promises to efficiently solve important problems. There are two persistent challenges in classical machine learning: the lack of labeled data, and the limit of computational power.
Objective: We propose a novel framework that resolves both issues: quantum semi-supervised learning. Moreover, we provide a protocol in systematically designing quantum machine learning algorithms with quantum supremacy, which can be extended beyond quantum semi-supervised learning.
Research method: We showcase two concrete quantum semi-supervised learning algorithms: a quantum self-training algorithm named the propagating nearest-neighbor classifier, and the quantum semi-supervised K-means clustering algorithm. By doing time complexity analysis, we conclude that they indeed possess quantum supremacy.
Prediction: By doing time complexity analysis, we conclude that they indeed
possess quantum supremacy.
arXiv paper 2110.02474
Problem: I model the belief formation and decision making processes of economic agents during a monetary policy regime change (an acceleration in the money supply) with a deep reinforcement learning algorithm in the AI literature.
Objective: I show that
when the money supply accelerates, the learning agents only adjust their
actions, which include consumption and demand for real balance, after gathering
learning experience for many periods.
Research questions: I also show that, 1. the AI agents who
explores their environment more adapt to the policy regime change quicker,
which leads to welfare improvements and less inflation volatility, and 2. the
AI agents who have experienced a structural change adjust their beliefs and
behaviours quicker than an inexperienced learning agent.
arXiv paper 2110.03135
Problem: Such label noise is due to the mismatch between the true label distribution of adversarial examples and the label inherited from clean examples - the true label distribution is distorted by the adversarial perturbation, but is neglected by the common practice that inherits labels from clean examples.
Objective: We show that label noise exists in adversarial training. Guided by our analyses, we
proposed a method to automatically calibrate the label to address the label
noise and robust overfitting. Our method achieves consistent performance
improvements across various models and datasets without introducing new
hyper-parameters or additional tuning.
Research method: Recognizing label noise sheds insights on the prevalence of robust overfitting in adversarial training, and explains its intriguing dependence on perturbation radius and data quality. Also, our label noise perspective aligns well with our observations of the epoch-wise double descent in adversarial training. Guided by our analyses, we proposed a method to automatically calibrate the label to address the label noise and robust overfitting.
arXiv paper 2110.05169
Problem: Deep Reinforcement Learning (RL) is mainly studied in a setting where the training and the testing environments are similar. But in many practical applications, these environments may differ. For instance, in control systems, the robot(s) on which a policy is learned might differ from the robot(s) on which a policy will run. It can be caused by different internal factors (e.g., calibration issues, system attrition, defective modules) or also by external changes (e.g., weather conditions). There is a need to develop RL methods that generalize well to variations of the training conditions.
Objective: In this article, we consider the simplest yet hard to tackle generalization setting where the test environment is unknown at train time, forcing the agent to adapt to the system's new dynamics.
arXiv paper 2110.08255
Problem: However, the current state of the art sparse Transformer architectures fail to couple down- and upsampling procedures to produce outputs in a similar resolution as the input.
Objective: We propose the Yformer model, based on a novel Y-shaped encoder-decoder architecture that (1) uses direct connection from the downscaled encoder layer to the corresponding upsampled decoder layer in a U-Net inspired architecture, (2) Combines the downscaling/upsampling with sparse attention to capture long-range effects, and (3) stabilizes the encoder-decoder stacks with the addition of an auxiliary reconstruction loss.