ali5341/scitldr-chat-format huggingface dataset:
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Iterative Target Augmentation for Effective Conditional Generation

Paper content:
Many challenging prediction problems, from molecular optimization to program synthesis, involve creating complex structured objects as outputs.
However, available training data may not be sufficient for a generative model to learn all possible complex transformations.
By leveraging the idea that evaluation is easier than generation, we show how a simple, broadly applicable, iterative target augmentation scheme can be surprisingly effective in guiding the training and use of such models.
Our scheme views the generative model as a prior distribution, and employs a separately trained filter as the likelihood.
In each augmentation step, we filter the model's outputs to obtain additional prediction targets for the next training epoch.
Our method is applicable in the supervised as well as semi-supervised settings.
We demonstrate that our approach yields significant gains over strong baselines both in molecular optimization and program synthesis.
In particular, our augmented model outperforms the previous state-of-the-art in molecular optimization by over 10% in absolute gain.

assistant: text: We improve generative models by proposing a meta-algorithm that filters new training data from the model's outputs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multi-scale Attributed Node Embedding

Paper content:
We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.
Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE).
Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes.
We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings.
Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.
We investigated attributed node embedding and proposes efficient pooled (AE) and multi-scale (MUSAE) attributed node embedding algorithms with linear runtime.
We proved that these algorithms implicitly factorize probability matrices of features appearing in the neighbourhood of nodes.
Two widely used neighbourhood preserving node embedding methods Perozzi et al. (2014; are in fact simplified cases of our models.
On several datasets (Wikipedia, Facebook, Github, and citation networks) we found that representations learned by our methods, in particular MUSAE, outperform neighbourhood based node embedding methods (Perozzi et al. (2014) ; Grover & Leskovec (2016) Our proposed embedding models are differentiated from other methods in that they encode feature information from higher order neighborhoods.
The most similar previous model BANE (Yang et al., 2018) encodes node attributes from higher order neighbourhoods but has non-linear runtime complexity and the product of adjacency matrix power and feature matrix is decomposed explicitly.
A PROOFS Lemma 1.
The empirical statistics of node-feature pairs obtained from random walks give unbiased estimates of joint probabilities of observing feature f ∈ F r steps
(i) after; or
(ii) before node v ∈ V, as given by:

assistant: text: We develop efficient multi-scale approximate attributed network embedding procedures with provable properties.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: REVISTING NEGATIVE TRANSFER USING ADVERSARIAL LEARNING

Paper content:
An unintended consequence of feature sharing is the model fitting to correlated tasks within the dataset, termed negative transfer.
In this paper, we revisit the problem of negative transfer in multitask setting and find that its corrosive effects are applicable to a wide range of linear and non-linear models, including neural networks.
We first study the effects of negative transfer in a principled way and show that previously proposed counter-measures are insufficient, particularly for trainable features.
We propose an adversarial training approach to mitigate the effects of negative transfer by viewing the problem in a domain adaptation setting.
Finally, empirical results on attribute prediction multi-task on AWA and CUB datasets further validate the need for correcting negative sharing in an end-to-end manner.

assistant: text: We look at negative transfer from a domain adaptation point of view to derive an adversarial learning algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking Data Augmentation: Self-Supervision and Self-Distillation

Paper content:
Data augmentation techniques, e.g., flipping or cropping, which systematically enlarge the training dataset by explicitly generating more training samples, are effective in improving the generalization performance of deep neural networks.
In the supervised setting, a common practice for data augmentation is to assign the same label to all augmented samples of the same source.
However, if the augmentation results in large distributional discrepancy among them (e.g., rotations), forcing their label invariance may be too difficult to solve and often hurts the performance.
To tackle this challenge, we suggest a simple yet effective idea of learning the joint distribution of the original and self-supervised labels of augmented samples.
The joint learning framework is easier to train, and enables an aggregated inference combining the predictions from different augmented samples for improving the performance.
Further, to speed up the aggregation process, we also propose a knowledge transfer technique, self-distillation, which transfers the knowledge of augmentation into the model itself.
We demonstrate the effectiveness of our data augmentation framework on various fully-supervised settings including the few-shot and imbalanced classification scenarios.

assistant: text: We propose a simple self-supervised data augmentation technique which improves performance of fully-supervised scenarios including few-shot learning and imbalanced classification.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PatchFormer: A neural architecture for self-supervised representation learning on images

Paper content:
Learning rich representations from predictive learning without labels has been a longstanding challenge in the field of machine learning.
Generative pre-training has so far not been as successful as contrastive methods in modeling representations of raw images.
In this paper, we propose a neural architecture for self-supervised representation learning on raw images called the PatchFormer which learns to model spatial dependencies across patches in a raw image.
Our method learns to model the conditional probability distribution of missing patches given the context of surrounding patches.
We evaluate the utility of the learned representations by fine-tuning the pre-trained model on low data-regime classification tasks.
Specifically, we benchmark our model on semi-supervised ImageNet classification which has become a popular benchmark recently for semi-supervised and self-supervised learning methods.
Our model is able to achieve 30.3% and 65.5% top-1 accuracies when trained only using 1% and 10% of the labels on ImageNet showing the promise for generative pre-training methods.

assistant: text: Decoding pixels can still work for representation learning on images
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning a set of interrelated tasks by using a succession of motor policies for a socially guided intrinsically motivated learner

Paper content:
We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies : Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB).
The learner can generalize over its experience to continuously learn new outcomes, by choosing actively what and how to learn guided by empirical measures of its own progress.
In this paper, we are considering the learning of a set of interrelated complex outcomes hierarchically organized.
We introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly more complex motor policies (combinations of primitive motor policies).
Our architecture can actively decide which outcome to focus on and which exploration strategy to apply.
Those strategies could be autonomous exploration, or active social guidance, where it relies on the expertise of a human teacher providing demonstrations at the learner's request.
We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand.
We also show that our "procedures" increases the agent's capability to learn complex tasks.

assistant: text: The paper describes a strategic intrinsically motivated learning algorithm which tackles the learning of complex motor policies.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Slow Thinking Enables Task-Uncertain Lifelong and Sequential Few-Shot Learning

Paper content:
Lifelong machine learning focuses on adapting to novel tasks without forgetting the old tasks, whereas few-shot learning strives to learn a single task given a small amount of data.
These two different research areas are crucial for artificial general intelligence, however, their existing studies have somehow assumed some impractical settings when training the models.
For lifelong learning, the nature (or the quantity) of incoming tasks during inference time is assumed to be known at training time.
As for few-shot learning, it is commonly assumed that a large number of tasks is available during training.
Humans, on the other hand, can perform these learning tasks without regard to the aforementioned assumptions.
Inspired by how the human brain works, we propose a novel model, called the Slow Thinking to Learn (STL), that makes sophisticated (and slightly slower) predictions by iteratively considering interactions between current and previously seen tasks at runtime.
Having conducted experiments, the results empirically demonstrate the effectiveness of STL for more realistic lifelong and few-shot learning settings.

assistant: text: This paper studies the interactions between the fast-learning and slow-prediction models and demonstrate how such interactions can improve machine capability to solve the joint lifelong and few-shot learning problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PowerSGD: Powered Stochastic Gradient Descent Methods for Accelerated Non-Convex Optimization

Paper content:
In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.
The proposed PowerSGD method simply raises the stochastic gradient to a certain power $\gamma\in[0,1]$ during iterations and introduces only one additional parameter, namely, the power exponent $\gamma$ (when $\gamma=1$, PowerSGD reduces to SGD).
We further propose PowerSGD with momentum, which we term \emph{PowerSGDM}, and provide convergence rate analysis on both PowerSGD and PowerSGDM methods.
Experiments are conducted on popular deep learning models and benchmark datasets.
Empirical results show that the proposed PowerSGD and PowerSGDM obtain faster initial training speed than adaptive gradient methods,  comparable generalization ability with SGD, and improved robustness to hyper-parameter selection and vanishing gradients.
PowerSGD is essentially a gradient modifier via a nonlinear transformation.
As such, it is orthogonal and complementary to other techniques for accelerating gradient-based optimization.

assistant: text: We propose a new class of optimizers for accelerated non-convex optimization via a nonlinear gradient transformation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Hypernetworks

Paper content:
We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
A Bayesian hypernetwork, h, is a neural network which learns to transform a simple noise distribution, p(e) = N(0,I), to a distribution q(t) := q(h(e)) over the parameters t of another neural network (the ``primary network).
We train q with variational inference, using an invertible h to enable efficient estimation of the variational lower bound on the posterior p(t | D) via sampling.
In contrast to most methods for Bayesian deep learning, Bayesian hypernets can represent a complex multimodal approximate posterior with correlations between parameters, while enabling cheap iid sampling of q(t).
In practice, Bayesian hypernets provide a better defense against adversarial examples than dropout, and also exhibit competitive performance on a suite of tasks which evaluate model uncertainty, including regularization, active learning, and anomaly detection.

assistant: text: We propose Bayesian hypernetworks: a framework for approximate Bayesian inference in neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Twin Networks: Matching the Future for Sequence Generation

Paper content:
We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a ``backward'' recurrent network to generate a given sequence in reverse order, and we encourage states of the forward model to predict cotemporal states of the backward model.
The backward network is used only during training, and plays no role during sampling or inference.
We hypothesize that our approach eases modeling of long-term dependencies by implicitly forcing the forward states to hold information about the longer-term future (as contained in the backward states).
We show empirically that our approach achieves 9% relative improvement for a speech recognition task, and achieves significant improvement on a COCO caption generation task.

assistant: text: The paper introduces a method of training generative recurrent networks that helps to plan ahead. We run a second RNN in a reverse direction and make a soft constraint between cotemporal forward and backward states.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: REFINING MONTE CARLO TREE SEARCH AGENTS BY MONTE CARLO TREE SEARCH

Paper content:
Reinforcement learning methods that continuously learn neural networks by episode generation with game tree search have been successful in two-person complete information deterministic games such as chess, shogi, and Go.
However, there are only reports of practical cases and there are little evidence to guarantee the stability and the final performance of learning process.
In this research, the coordination of episode generation was focused on.
By means of regarding the entire system as game tree search, the new method can handle the trade-off between exploitation and exploration during episode generation.
The experiments with a small problem showed that it had robust performance compared to the existing method, Alpha Zero.

assistant: text: Apply Monte Carlo Tree Search to episode generation in Alpha Zero
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: CopyCAT: Taking Control of Neural Policies with Constant Attacks

Paper content:
We propose a new perspective on adversarial attacks against deep reinforcement learning agents.
Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy.
It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario.
We show its effectiveness on Atari 2600 games in the novel read-only setting.
In the latter, the adversary cannot directly modify the agent's state -its representation of the environment- but can only attack the agent's observation -its perception of the environment.
Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.

assistant: text: We propose a new attack for taking full control of neural policies in realistic settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Post-training for Deep Learning

Paper content:
One of the main challenges of deep learning methods is the choice of an appropriate training strategy.
In particular, additional steps, such as unsupervised pre-training, have been shown to greatly improve the performances of deep structures.
In this article, we propose an extra training step, called post-training, which only optimizes the last layer of the network.
We show that this procedure can be analyzed in the context of kernel theory, with the first layers computing an embedding of the data and the last layer a statistical model to solve the task based on this embedding.
This step makes sure that the embedding, or representation, of the data is used in the best possible way for the considered task.
This idea is then tested on multiple architectures with various data sets, showing that it consistently provides a boost in performance.

assistant: text: We propose an additional training step, called post-training, which computes optimal weights for the last layer of the network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Faster Discovery of Neural Architectures by Searching for Paths in a Large Model

Paper content:
We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods.
In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model.
The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set.
Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss.
On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank.
On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017).
Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.

assistant: text: An approach that speeds up neural architecture search by 10x, whilst using 100x less computing resource.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces

Paper content:
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge.
For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced.
We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.
TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications.
With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding.
We achieve new state-of-the-art performances with signficant margins in Link Prediction and Triple Classification on FB122 dataset, with boosted performance even on test instances that cannot be inferred by logical rules.
The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.

assistant: text: We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space in an explainable, robust, and geometrically coherent way.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discriminator Based Corpus Generation for General Code Synthesis

Paper content:
Current work on neural code synthesis consists of increasingly sophisticated architectures being trained on highly simplified domain-specific languages, using uniform sampling across program space of those languages for training.
By comparison, program space for a C-like language is vast, and extremely sparsely populated in terms of `useful' functionalities; this requires a far more intelligent approach to corpus generation for effective training.
We use a genetic programming approach using an iteratively retrained discriminator to produce a population suitable as labelled training data for a neural code synthesis architecture.
We demonstrate that use of a discriminator-based training corpus generator, trained using only unlabelled problem specifications in classic Programming-by-Example format, greatly improves network performance compared to current uniform sampling techniques.

assistant: text: A way to generate training corpora for neural code synthesis using a discriminator trained on unlabelled data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sampling-Free Learning of Bayesian Quantized Neural Networks

Paper content:
Bayesian learning of model parameters in neural networks is important in scenarios where estimates with well-calibrated uncertainty are important.
In this paper, we propose Bayesian quantized networks (BQNs), quantized neural networks (QNNs) for which we learn a posterior distribution over their discrete parameters.
We provide a set of efficient algorithms for learning and prediction in BQNs without the need to sample from their parameters or activations, which not only allows for differentiable learning in quantized models but also reduces the variance in gradients estimation.
We evaluate BQNs on MNIST, Fashion-MNIST and KMNIST classification datasets compared against bootstrap ensemble of QNNs (E-QNN).
We demonstrate BQNs achieve both lower predictive errors and better-calibrated uncertainties than E-QNN (with less than 20% of the negative log-likelihood).

assistant: text: We propose Bayesian quantized networks, for which we learn a posterior distribution over their quantized parameters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Global Convergence of Policy Gradient Methods for Linearized  Control Problems

Paper content:
Direct policy gradient methods for reinforcement learning and continuous control problems are a popular
approach for a variety of reasons: 
1) they are easy to implement without explicit knowledge of the underlying model;
2) they are an "end-to-end" approach, directly optimizing the performance metric of interest;
3) they inherently allow for richly parameterized policies.
A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives.
In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties.
This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.

assistant: text: This paper shows that model-free policy gradient methods can converge to the global optimal solution for non-convex linearized control problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Relationship between Self-Attention and Convolutional Layers

Paper content:
Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block.
Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks.
This raises the question: do learned attention layers operate similarly to convolutional layers?
This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.
Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer.
Our numerical experiments then show that self-attention layers attend to pixel-grid patterns similarly to CNN layers, corroborating our analysis.
Our code is publicly available.

assistant: text: A self-attention layer can perform convolution and often learns to do so in practice.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Learning via Meta-Learning

Paper content:
A central goal of unsupervised learning is to acquire representations from unlabeled data or experience that can be used for more effective learning of downstream tasks from modest amounts of labeled data.
Many prior unsupervised learning works aim to do so by developing proxy objectives based on reconstruction, disentanglement, prediction, and other metrics.
Instead, we develop an unsupervised meta-learning method that explicitly optimizes for the ability to learn a variety of tasks from small amounts of data.
To do so, we construct tasks from unlabeled data in an automatic way and run meta-learning over the constructed tasks.
Surprisingly, we find that, when integrated with meta-learning, relatively simple task construction mechanisms, such as clustering embeddings, lead to good performance on a variety of downstream, human-specified tasks.
Our experiments across four image datasets indicate that our unsupervised meta-learning approach acquires a learning algorithm without any labeled data that is applicable to a wide range of downstream classification tasks, improving upon the embedding learned by four prior unsupervised learning methods.

assistant: text: An unsupervised learning method that uses meta-learning to enable efficient learning of downstream image classification tasks, outperforming state-of-the-art methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Sliced Wasserstein Auto-Encoders

Paper content:
In this paper we use the geometric properties of the optimal transport (OT) problem and the Wasserstein distances to define a prior distribution for the latent space of an auto-encoder.
We introduce Sliced-Wasserstein Auto-Encoders (SWAE), that enable one to shape the distribution of the latent space into any samplable probability distribution without the need for training an adversarial network or having a likelihood function specified.
In short, we regularize the auto-encoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a samplable prior distribution.
We show that the proposed formulation has an efficient numerical solution that provides similar capabilities to Wasserstein Auto-Encoders (WAE) and Variational Auto-Encoders (VAE), while benefiting from an embarrassingly simple implementation.
We provide extensive error analysis for our algorithm, and show its merits on three benchmark datasets.
We introduced Sliced Wasserstein auto-encoders (SWAE), which enable one to shape the distribution of the encoded samples to any samplable distribution without the need for adversarial training or having a likelihood function specified.
In addition, we provided a simple and efficient numerical scheme for this problem, which only relies on few inner products and sorting operations in each SGD iteration.
We further demonstrated the capability of our method on three image datasets, namely the MNIST, the CelebA face, and the LSUN Bedroom datasets, and showed competitive performance, in the sense of matching distributions p Z and q Z , to the techniques that rely on additional adversarial trainings.
Finally, we envision SWAE could be effectively used in transfer learning and domain adaptation algorithms where q Z comes from a source domain and the task is to encode the target domain p X in a latent space such that the distribution follows the distribution of the target domain.
Figure 4: Sample convergence behavior for our method compared to the WAE-GAN, where q Z is set to a ring distribution FIG5 , top left).
The columns represent batch iterations (batchsize= 500).The
top half of the table shows results of ψ(z) for z ∼ q Z , and the bottom half shows z ∼ q Z and φ(x) for x ∼ p X . It
can be seen that the adversarial loss in the latent space does not provide a full coverage of the distribution, which is a similar problem to the well-known 'mode collapse' problem in the GANs. It
can be seen that SWAE provides a superior match between p Z and q Z while it does not require adversarial training.

assistant: text: In this paper we use the sliced-Wasserstein distance to shape the latent distribution of an auto-encoder into any samplable prior distribution.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gating Revisited: Deep Multi-layer RNNs That Can Be Trained

Paper content:
Recurrent Neural Networks (RNNs) are widely used models for sequence data.
Just like for feedforward networks, it has become common to build "deep" RNNs, i.e., stack multiple recurrent layers to obtain higher-level abstractions of the data.
However, this works only for a handful of layers.
Unlike feedforward networks, stacking more than a few recurrent units (e.g., LSTM cells) usually hurts model performance, the reason being vanishing or exploding gradients during training.
We investigate the training of multi-layer RNNs and examine the magnitude of the gradients as they propagate through the network.
We show that, depending on the structure of the basic recurrent unit, the gradients are systematically attenuated or amplified, so that with an increasing depth they tend to vanish, respectively explode.
Based on our analysis we design a new type of gated cell that better preserves gradient magnitude, and therefore makes it possible to train deeper RNNs.
We experimentally validate our design with five different sequence modelling tasks on three different datasets.
The proposed stackable recurrent (STAR) cell allows for substantially deeper recurrent architectures, with improved performance.

assistant: text: We analyze the gradient propagation in deep RNNs and from our analysis, we propose a new multi-layer deep RNN.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Natural Language Adversarial Attack and Defense in Word Level

Paper content:
Up until very recently, inspired by a mass of researches on adversarial examples for computer vision, there has been a growing interest in designing adversarial attacks for Natural Language Processing (NLP) tasks, followed by very few works of adversarial defenses for NLP.
To our knowledge, there exists no defense method against the successful synonym substitution based attacks that aim to satisfy all the lexical, grammatical, semantic constraints and thus are hard to perceived by humans.
We contribute to fill this gap and propose a novel adversarial defense method called Synonym Encoding Method (SEM), which inserts an encoder before the input layer of the model and then trains the model to eliminate adversarial perturbations.
Extensive experiments demonstrate that SEM can efficiently defend current best synonym substitution based adversarial attacks with little decay on the accuracy for benign examples.
To better evaluate SEM, we also design a strong attack method called Improved Genetic Algorithm (IGA) that adopts the genetic metaheuristic for synonym substitution based attacks.
Compared with existing genetic based adversarial attack, IGA can achieve higher attack success rate while maintaining the transferability of the adversarial examples.

assistant: text: The first text adversarial defense method in word level, and the improved generic based attack method against synonyms substitution based attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Informed Temporal Modeling via Logical Specification of Factorial LSTMs

Paper content:
Consider a world in which events occur that involve various entities.
Learning how to predict future events from patterns of past events becomes more difficult as we consider more types of events.
Many of the patterns detected in the dataset by an ordinary LSTM will be spurious since the number of potential pairwise correlations, for example, grows quadratically with the number of events.
We propose a type of factorial LSTM architecture where different blocks of LSTM cells are responsible for capturing different aspects of the world state.
We use Datalog rules to specify how to derive the LSTM structure from a database of facts about the entities in the world.
This is analogous to how a probabilistic relational model (Getoor & Taskar, 2007) specifies a recipe for deriving a graphical model structure from a database.
In both cases, the goal is to obtain useful inductive biases by encoding informed independence assumptions into the model.
We specifically consider the neural Hawkes process, which uses an LSTM to modulate the rate of instantaneous events in continuous time.
In both synthetic and real-world domains, we show that we obtain better generalization by using appropriate factorial designs specified by simple Datalog programs.

assistant: text: Factorize LSTM states and zero-out/tie LSTM weight matrices according to real-world structural biases expressed by Datalog programs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning

Paper content:
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.
A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities.
Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities, or evaluating the truth of a proposed triple.
Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them.
We propose a new algorithm, MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity.
Since random walks are impractical in a setting with unknown destination and combinatorially many paths from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths.
On a comprehensive evaluation on seven knowledge base datasets, we found MINERVA to be competitive with many current state-of-the-art methods.

assistant: text: We present a RL agent MINERVA which learns to walk on a knowledge graph and answer queries
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Domain-independent Plan Intervention When Users Unwittingly Facilitate Attacks

Paper content:
In competitive situations, agents may take actions to achieve their goals that unwittingly facilitate an opponent’s goals.
We
consider a domain where three agents operate: (1) a user (human), (2) an attacker (human or a software) agent and (3) an observer (a software) agent.
The user and the attacker compete to achieve different goals.
When there is a disparity in the domain knowledge the user and the attacker possess, the attacker may use the user’s unfamiliarity with the domain to
its advantage and further its own goal.
In this situation, the observer, whose goal is to support the user may need to intervene, and this intervention needs to occur online, on-time and be accurate.
We formalize the online plan intervention problem and propose a solution that uses a decision tree classifier to identify intervention points in situations where agents unwittingly facilitate an opponent’s goal.
We trained a classifier using domain-independent features extracted from the observer’s decision space to evaluate the “criticality” of the current state.
The trained model is then used in an online setting on IPC benchmarks to identify observations that warrant intervention.
Our contributions lay a foundation for further work in the area of deciding when to intervene.

assistant: text: We introduce a machine learning model that uses domain-independent features to estimate the criticality of the current state to cause a known undesirable state.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Classifier-to-Generator Attack: Estimation of Training Data Distribution from Classifier

Paper content:
Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons.
In this setting, can an adversary obtain the private samples if the classification model is given to the adversary?
We call this reverse engineering against the classification model the Classifier-to-Generator (C2G) Attack.
This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication).
For C2G attack, we introduce a novel GAN, PreImageGAN.
In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model $f$, $P(X|f(X)=y)$, where $X$ is the random variable on the sample space and $y$ is the probability vector representing the target label arbitrary specified by the adversary.
In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition.
In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images.
In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.

assistant: text: Estimation of training data distribution from trained classifier using GAN.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Convolutional Conditional Neural Processes

Paper content:
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data.
Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images.
The model embeds data sets into an infinite-dimensional function space, as opposed to finite-dimensional vector spaces.
To formalize this notion, we extend the theory of neural representations of sets to include functional representations, and demonstrate that any translation-equivariant embedding can be represented using a convolutional deep-set.
We evaluate ConvCNPs in several settings, demonstrating that they achieve state-of-the-art performance compared to existing NPs.
We demonstrate that building in translation equivariance enables zero-shot generalization to challenging, out-of-domain tasks.

assistant: text: We extend deep sets to functional embeddings and Neural Processes to include translation equivariant members
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analyzing Federated Learning through an Adversarial Lens

Paper content:
Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server.
In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence.
We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's update to overcome the effects of other agents' updates.
To increase attack stealth, we propose an alternating minimization strategy, which alternately optimizes for the training loss and the adversarial objective.
We follow up by using parameter estimation for the benign agents' updates to improve on attack success.
Finally, we use a suite of interpretability techniques to generate visual explanations of model decisions for both benign and malicious models and show that the explanations are nearly visually indistinguishable.
Our results indicate that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth, thus highlighting the vulnerability of the federated learning setting and the need to develop effective defense strategies.

assistant: text: Effective model poisoning attacks on federated learning able to cause high-confidence targeted misclassification of desired inputs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Low Shot Learning with Untrained Neural Networks for Imaging Inverse Problems

Paper content:
Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks.
However, very few works have considered how to interpolate between these no- to high-data regimes.
In particular, how can one use the availability of a small amount of data (even 5-25 examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well?
In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest.
Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization.
Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than 1% of the data needed to train a generative model.

assistant: text: We show how pre-training an untrained neural network with as few as 5-25 examples can improve reconstruction results in compressed sensing and semantic recovery problems like colorization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Variational inference of latent hierarchical dynamical systems in neuroscience: an application to calcium imaging data

Paper content:
A key problem in neuroscience, and life sciences more generally, is that data is generated by a hierarchy of dynamical systems.
One example of this is in \textit{in-vivo} calcium imaging data, where data is generated by a lower-order dynamical system governing calcium flux in neurons, which itself is driven by a higher-order dynamical system of neural computation.
Ideally, life scientists would be able to infer the dynamics of both the lower-order systems and the higher-order systems, but this is difficult in high-dimensional regimes.
A recent approach using sequential variational auto-encoders demonstrated it was possible to learn the latent dynamics of a single dynamical system for computations during reaching behaviour in the brain, using spiking data modelled as a Poisson process.
Here we extend this approach using a ladder method to infer a hierarchy of dynamical systems, allowing us to capture calcium dynamics as well as neural computation.
In this approach, spiking events drive lower-order calcium dynamics, and are themselves controlled by a higher-order latent dynamical system.
We generate synthetic data by generating firing rates, sampling spike trains, and converting spike trains to fluorescence transients, from two dynamical systems that have been used as key benchmarks in recent literature: a Lorenz attractor, and a chaotic recurrent neural network.
We show that our model is better able to reconstruct Lorenz dynamics from fluorescence data than competing methods.
However, though our model can reconstruct underlying spike rates and calcium transients from the chaotic neural network well, it does not perform as well at reconstructing firing rates as basic techniques for inferring spikes from calcium data.
These results demonstrate that VLAEs are a promising approach for modelling hierarchical dynamical systems data in the life sciences, but that inferring the dynamics of lower-order systems can potentially be better achieved with simpler methods.

assistant: text: We extend a successful recurrent variational autoencoder for dynamic systems to model an instance of dynamic systems hierarchy in neuroscience using the ladder method.