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

Title: Building Hierarchical Interpretations in Natural Language via Feature Interaction Detection

Paper content:
The interpretability of neural networks has become crucial for their applications in real world with respect to the reliability and trustworthiness.
Existing explanation generation methods usually provide important features by scoring their individual contributions to the model prediction and ignore the interactions between features, which eventually provide a bag-of-words representation as explanation.
In natural language processing, this type of explanations is challenging for human user to understand the meaning of an explanation and draw the connection between explanation and model prediction, especially for long texts.
In this work, we focus on detecting the interactions between features, and propose a novel approach to build a hierarchy of explanations based on feature interactions.
The proposed method is evaluated with three neural classifiers, LSTM, CNN, and BERT, on two benchmark text classification datasets.
The generated explanations are assessed by both automatic evaluation measurements and human evaluators.
Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models, and understandable to humans.

assistant: text: An interpretation method based on feature interactions and feature importance score as compared to independent feature contributions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Learning of Entailment-Vector Word Embeddings

Paper content:
Entailment vectors are a principled way to encode in a vector what information is known and what is unknown.
They are designed to model relations where one vector should include all the information in another vector, called entailment.
This paper investigates the unsupervised learning of entailment vectors for the semantics of words.
Using simple entailment-based models of the semantics of words in text (distributional semantics), we induce entailment-vector word embeddings which outperform the best previous results for predicting entailment between words, in unsupervised and semi-supervised experiments on hyponymy.

assistant: text: The paper presents a word embedding algorithm for lexical entailment which follows the work of Henderson and Popa (ACL, 2016).
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Context Mover's Distance & Barycenters: Optimal transport of contexts for building representations

Paper content:
We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a fixed length vector.
In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space.
This enables us to consider the problem of representation learning with a perspective from Optimal Transport and take advantage of its numerous tools such as Wasserstein distance and Wasserstein barycenters.
We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance quantitatively as well as qualitatively on tasks such as measuring sentence similarity and word entailment, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec and GenSen).
The key benefits of the proposed approach include:
(a) capturing uncertainty and polysemy via modeling the entities as distributions,
(b) utilizing the underlying geometry of the particular task (with the ground cost),
(c) simultaneously providing interpretability with the notion of optimal transport between contexts and
(d) easy applicability on top of existing point embedding methods.
In essence, the framework can be useful for any unsupervised or supervised problem (on text or other modalities); and only requires a co-occurrence structure inherent to many problems.
The code, as well as pre-built histograms, are available under https://github.com/context-mover.

assistant: text: Represent each entity as a probability distribution over contexts embedded in a ground space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

Paper content:
Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.
In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base.
We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document.
NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference.
To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset.
Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.

assistant: text: Uses a GRU autoencoder to represent the "context" (related enitities of a given disease within the span of a sentence), solving the BioNLP task with significant improvements over the best-known methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Time-Aware Assistance Functions for Numerical Fluid Solvers

Paper content:
Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems.
A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena.
This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers.
The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data.
We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data.
We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver.
While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.

assistant: text: We introduce a neural network approach to assist partial differential equation solvers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding Attention Mechanisms

Paper content:
Attention mechanisms have advanced the state of the art in several machine learning tasks.
Despite significant empirical gains, there is a lack of theoretical analyses on understanding their effectiveness.
In this paper, we address this problem by studying the landscape of population and empirical loss functions of attention-based neural networks.
Our results show that, under mild assumptions, every local minimum of a two-layer global attention model has low prediction error, and attention models require lower sample complexity than models not employing attention.
We then extend our analyses to the popular self-attention model, proving that they deliver consistent predictions with a more expressive class of functions.
Additionally, our theoretical results provide several guidelines for designing attention mechanisms.
Our findings are validated with satisfactory experimental results on MNIST and IMDB reviews dataset.

assistant: text: We analyze the loss landscape of neural networks with attention and explain why attention is helpful in training neural networks to achieve good performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Analyzing Inverse Problems with Invertible Neural Networks

Paper content:
For many applications, in particular in natural science, the task is to
determine hidden system parameters from a set of measurements.
Often,
the forward process from parameter- to measurement-space is well-defined,
whereas the inverse problem is ambiguous: multiple parameter sets can
result in the same measurement.
To fully characterize this ambiguity, the full
posterior parameter distribution, conditioned on an observed measurement,
has to be determined.
We argue that a particular class of neural networks
is well suited for this task – so-called Invertible Neural Networks (INNs).
Unlike classical neural networks, which attempt to solve the ambiguous
inverse problem directly, INNs focus on learning the forward process, using
additional latent output variables to capture the information otherwise
lost.
Due to invertibility, a model of the corresponding inverse process is
learned implicitly.
Given a specific measurement and the distribution of
the latent variables, the inverse pass of the INN provides the full posterior
over parameter space.
We prove theoretically and verify experimentally, on
artificial data and real-world problems from medicine and astrophysics, that
INNs are a powerful analysis tool to find multi-modalities in parameter space,
uncover parameter correlations, and identify unrecoverable parameters.

assistant: text: To analyze inverse problems with Invertible Neural Networks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Lifelong Word Embedding via Meta-Learning

Paper content:
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks.
On one hand, traditional word embeddings are trained on a large scale corpus for general-purpose tasks, which are often sub-optimal for many domain-specific tasks.
On the other hand, many domain-specific tasks do not have a large enough domain corpus to obtain high-quality embeddings.
We observe that domains are not isolated and a small domain corpus can leverage the learned knowledge from many past domains to augment that corpus in order to generate high-quality embeddings.
In this paper, we formulate the learning of word embeddings as a lifelong learning process.
Given knowledge learned from many previous domains and a small new domain corpus, the proposed method can effectively generate new domain embeddings by leveraging a simple but effective algorithm and a meta-learner, where the meta-learner is able to provide word context similarity information at the domain-level.
Experimental results demonstrate that the proposed method can effectively learn new domain embeddings from a small corpus and past domain knowledges\footnote{We will release the code after final revisions.}.
We
also demonstrate that general-purpose embeddings trained from a large scale corpus are sub-optimal in domain-specific tasks.

assistant: text: learning better domain embeddings via lifelong learning and meta-learning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Top-Down Neural Model For Formulae

Paper content:
We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.
The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.
The results of this network are then processed by two recurrent neural networks.
One of the interesting aspects of our model is how propositional atoms are treated.
For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

assistant: text: A top-down approach how to recursively represent propositional formulae by neural networks is presented.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Adversarially Robust Neural Networks via Optimal Control: Bridging Robustness with Lyapunov Stability

Paper content:
Deep neural networks are known to be vulnerable to adversarial perturbations.
In this paper, we bridge adversarial robustness of neural nets with Lyapunov stability of dynamical systems.
From this viewpoint, training neural nets is equivalent to finding an optimal control of the discrete dynamical system, which allows one to utilize methods of successive approximations, an optimal control algorithm based on Pontryagin's maximum principle, to train neural nets.
This decoupled training method allows us to add constraints to the optimization, which makes the deep model more robust.
The constrained optimization problem can be formulated as a semi-definite programming problem and hence can be solved efficiently.
Experiments show that our method effectively improves deep model's adversarial robustness.

assistant: text: An adversarial defense method bridging robustness of deep neural nets with Lyapunov stability
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: NormCo: Deep Disease Normalization for Biomedical Knowledge Base Construction

Paper content:
Biomedical knowledge bases are crucial in modern data-driven biomedical sciences, but auto-mated biomedical knowledge base construction remains challenging.
In this paper, we consider the problem of disease entity normalization, an essential task in constructing a biomedical knowledge base.
We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document.
NormCo mod-els entity mentions using a simple semantic model which composes phrase representations from word embeddings, and treats coherence as a disease concept co-mention sequence using an RNN rather than modeling the joint probability of all concepts in a document, which requires NP-hard inference.
To overcome the issue of data sparsity, we used distantly supervised data and synthetic data generated from priors derived from the BioASQ dataset.
Our experimental results show thatNormCo outperforms state-of-the-art baseline methods on two disease normalization corpora in terms of (1) prediction quality and (2) efficiency, and is at least as performant in terms of accuracy and F1 score on tagged documents.

assistant: text: We present NormCo, a deep coherence model which considers the semantics of an entity mention, as well as the topical coherence of the mentions within a single document to perform disease entity normalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Alexandria: Unsupervised High-Precision Knowledge Base Construction using a Probabilistic Program

Paper content:
Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.
In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.
Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.
Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.
The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.
Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.
We demonstrate this by constructing a high precision (typically 97\%+) knowledge base for people from a single seed fact.

assistant: text: This paper uses a probabilistic program describing the process by which facts describing entities can be realised in text and large number of web pages, to learn to perform fact extraction about people using a single seed fact.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Robust Natural Language Representation Learning for Natural Language Inference by Projecting Superficial Words out

Paper content:
In natural language inference, the semantics of some words do not affect the inference.
Such information is considered superficial and brings overfitting.
How can we represent and discard such superficial information?
In this paper, we use first order logic (FOL) - a classic technique from meaning representation language – to explain what information is superficial for a given sentence pair.
Such explanation also suggests two inductive biases according to its properties.
We proposed a neural network-based approach that utilizes the two inductive biases.
We obtain substantial improvements over extensive experiments.

assistant: text: This paper tries to reduce superficial information in natural language inference to prevent overfitting, and introduces a graph neural network to model relation between premise and hypothesis.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks

Paper content:
Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data.
However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise).
In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help.
We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.
We call this approach limited self-supervision, as we limit ourselves to only the data at-hand.
We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data.
Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task.
Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains.
In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62).
Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.

assistant: text: We show that extra unlabeled data is not required for self-supervised auxiliary tasks to be useful for time series classification, and present new and effective auxiliary tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Disentangled Representation Learning with Information Maximizing Autoencoder

Paper content:
Learning disentangled representation from any unlabelled data is a non-trivial problem.
In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion.
We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.

assistant: text: The authors present a framework in which an auto encoder (E, D) is regularized such that its latent representation to share mutual information with a generated latent space representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visual Imitation with a Minimal Adversary

Paper content:
High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.
In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?
We show that adversarial imitation can work well even in this high dimensional observation space.
Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.
Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.
Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

assistant: text: The paper proposes to use a "minimal adversary" in generative adversarial imitation learning under high-dimensional visual spaces.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Understanding the (Un)interpretability of Natural Image Distributions Using Generative Models

Paper content:
Probability density estimation is a classical and well studied problem, but standard density estimation methods have historically lacked the power to model complex and high-dimensional image distributions.
More recent generative models leverage the power of neural networks to implicitly learn and represent probability models over complex images.
We describe methods to extract explicit probability density estimates from GANs, and explore the properties of these image density functions.
We perform sanity check experiments to provide evidence that these probabilities are reasonable.
However, we also show that density functions of natural images are difficult to interpret and thus limited in use.
We study reasons for this lack of interpretability, and suggest that we can get better interpretability by doing density estimation on latent representations of images.

assistant: text: We examine the relationship between probability density values and image content in non-invertible GANs.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer

Paper content:
In this work, we address the problem of musical timbre transfer, where the goal is to manipulate the timbre of a sound sample from one instrument to match another instrument while preserving other musical content, such as pitch, rhythm, and loudness.
In principle, one could apply image-based style transfer techniques to a time-frequency representation of an audio signal, but this depends on having a representation that allows independent manipulation of timbre as well as high-quality waveform generation.
We introduce TimbreTron, a method for musical timbre transfer which applies “image” domain style transfer to a time-frequency representation of the audio signal, and then produces a high-quality waveform using a conditional WaveNet synthesizer.
We show that the Constant Q Transform (CQT) representation is particularly well-suited to convolutional architectures due to its approximate pitch equivariance.
Based on human perceptual evaluations, we confirmed that TimbreTron recognizably transferred the timbre while otherwise preserving the musical content, for both monophonic and polyphonic samples.
We made an accompanying demo video here: https://www.cs.toronto.edu/~huang/TimbreTron/index.html which we strongly encourage you to watch before reading the paper.

assistant: text: We present the TimbreTron, a pipeline for perfoming high-quality timbre transfer on musical waveforms using CQT-domain style transfer.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LatentPoison -- Adversarial Attacks On The Latent Space

Paper content:
Robustness and security of machine learning (ML) systems are intertwined, wherein a non-robust ML system (classifiers, regressors, etc.) can be subject to attacks using a wide variety of exploits.
With the advent of scalable deep learning methodologies, a lot of emphasis has been put on the robustness of supervised, unsupervised and reinforcement learning algorithms.
Here, we study the robustness of the latent space of a deep variational autoencoder (dVAE), an unsupervised generative framework, to show that it is indeed possible to perturb the latent space, flip the class predictions and keep the classification probability approximately equal before and after an attack.
This means that an agent that looks at the outputs of a decoder would remain oblivious to an attack.

assistant: text: This paper concerns security and machine learning and proposes a man-in-middle attack that alters the VAE encoding of input data so that decoded output will be misclassified.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BlackMarks: Black-box Multi-bit Watermarking for Deep Neural Networks

Paper content:
Deep Neural Networks (DNNs) are increasingly deployed in cloud servers and autonomous agents due to their superior performance.
The deployed DNN is either leveraged in a white-box setting (model internals are publicly known) or a black-box setting (only model outputs are known) depending on the application.
A practical concern in the rush to adopt DNNs is protecting the models against Intellectual Property (IP) infringement.
We propose BlackMarks, the first end-to-end multi-bit watermarking framework that is applicable in the black-box scenario.
BlackMarks takes the pre-trained unmarked model and the owner’s binary signature as inputs.
The output is the corresponding marked model with specific keys that can be later used to trigger the embedded watermark.
To do so, BlackMarks first designs a model-dependent encoding scheme that maps all possible classes in the task to bit ‘0’ and bit ‘1’.
Given the owner’s watermark signature (a binary string), a set of key image and label pairs is designed using targeted adversarial attacks.
The watermark (WM) is then encoded in the distribution of output activations of the DNN by fine-tuning the model with a WM-specific regularized loss.
To extract the WM, BlackMarks queries the model with the WM key images and decodes the owner’s signature from the corresponding predictions using the designed encoding scheme.
We perform a comprehensive evaluation of BlackMarks’ performance on MNIST, CIFAR-10, ImageNet datasets and corroborate its effectiveness and robustness.
BlackMarks preserves the functionality of the original DNN and incurs negligible WM embedding overhead as low as 2.054%.

assistant: text: Proposing the first watermarking framework for multi-bit signature embedding and extraction using the outputs of the DNN.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Disentangled Representation Learning with Information Maximizing Autoencoder

Paper content:
Learning disentangled representation from any unlabelled data is a non-trivial problem.
In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsupervised fashion.
We have evaluated our model on MNIST dataset and achieved approximately 98.9 % test accuracy while using complete unsupervised training.

assistant: text: Learn disentangle representation in an unsupervised manner.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Why do These Match? Explaining the Behavior of Image Similarity Models

Paper content:
Explaining a deep learning model can help users understand its behavior and allow researchers to discern its shortcomings.
Recent work has primarily focused on explaining models for tasks like image classification or visual question answering.
In this paper, we introduce an explanation approach for image similarity models, where a model's output is a score measuring the similarity of two inputs rather than a classification.
In this task, an explanation depends on both of the input images, so standard methods do not apply.
We propose an explanation method that pairs a saliency map identifying important image regions with an attribute that best explains the match.
We find that our explanations provide additional information not typically captured by saliency maps alone, and can also improve performance on the classic task of attribute recognition.
Our approach's ability to generalize is demonstrated on two datasets from diverse domains, Polyvore Outfits and Animals with Attributes 2.

assistant: text: A black box approach for explaining the predictions of an image similarity model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Global Convergence  of Training Deep Linear ResNets

Paper content:
We study the convergence of gradient descent (GD) and stochastic gradient descent (SGD) for training $L$-hidden-layer linear residual networks (ResNets).
We prove that for training deep residual networks with certain linear transformations at input and output layers, which are fixed throughout training, both GD and SGD with zero initialization on all hidden weights can converge to the global minimum of the training loss.
Moreover, when specializing to appropriate Gaussian random linear transformations, GD and SGD provably optimize wide enough deep linear ResNets.
Compared with the global convergence result of GD for training standard deep linear networks \citep{du2019width}, our condition on the neural network width is sharper by a factor of $O(\kappa L)$, where $\kappa$ denotes the condition number of the covariance matrix of the training data.
In addition, for the first time we establish the global convergence of SGD for training deep linear ResNets and prove a linear convergence rate when the global minimum is $0$.

assistant: text: Under certain condition on the input and output linear transformations, both GD and SGD can achieve global convergence for training deep linear ResNets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Enhancing the Transferability of Adversarial Examples with Noise Reduced Gradient

Paper content:
Deep neural networks provide state-of-the-art performance for many applications of interest.
Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.
Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models.
Consequently  the adversary can leverage it to attack against the deployed black-box systems.
In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability.
Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component.
Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically.
We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance.
These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.

assistant: text: We propose a new method for enhancing the transferability of adversarial examples by using the noise-reduced gradient.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Not-So-Random Features

Paper content:
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.
Our method produces a sequence of feature maps, iteratively refining the SVM margin.
We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.
Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.
Choosing the right kernel is a classic question that has riddled machine learning practitioners and theorists alike.
Conventional wisdom instructs the user to select a kernel which captures the structure and geometric invariances in the data.
Efforts to formulate this principle have inspired vibrant areas of study, going by names from feature selection to multiple kernel learning (MKL).We
present a new, principled approach for selecting a translation-invariant or rotation-invariant kernel to maximize the SVM classification margin. We
first describe a kernel-alignment subroutine, which finds a peak in the Fourier transform of an adversarially chosen data-dependent measure. Then
, we define an iterative procedure that produces a sequence of feature maps, progressively improving the margin. The
resulting algorithm is strikingly simple and scalable. Intriguingly
, our analysis interprets the main algorithm as no-regret learning dynamics in a zero-sum min-max game, whose value is the classification margin. Thus, we are
able to quantify convergence guarantees towards the largest margin realizable by a kernel with the assumed invariance. Finally, we
exhibit experiments on synthetic and benchmark datasets, demonstrating consistent improvements over related random features-based kernel methods.
We have presented an efficient kernel learning method that uses tools from Fourier analysis and online learning to optimize over two natural infinite families of kernels.
With this method, we show meaningful improvements on benchmark tasks, compared to related random features-based methods.
Many theoretical questions remain, such as accelerating the search for Fourier peaks (e.g. Hassanieh et al. FORMULA1 ; Kapralov FORMULA1 ).
These, in addition to applying our learned kernels to state-of-the-art methods (e.g. convolutional kernel networks (Mairal et al., 2014; BID21 Mairal, 2016) ), prove to be exciting directions for future work.

assistant: text: The paper proposes to learn a custom translation or rotation invariant kernal in the Fourier representation to maximize the margin of SVM.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mapping the hyponymy relation of wordnet onto vector Spaces

Paper content:
In this paper, we investigate mapping the hyponymy relation of
 wordnet to feature vectors.
We aim to model lexical knowledge in such a way that it can be used as
  input in generic machine-learning models, such as phrase entailment
  predictors.
We propose two models.
The first one leverages an existing mapping of
  words to feature vectors (fasttext), and attempts to classify
  such vectors as within or outside of each class.
The second model is fully supervised,
  using solely wordnet as a ground truth.
It maps each concept to an
  interval or a disjunction thereof.
On the first model, we approach, but not quite attain state of the
  art performance.
The second model can achieve near-perfect accuracy.

assistant: text: This paper studies how hyponymy between words can be mapped to feature representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Paper content:
Neural Tangents is a library designed to enable research into infinite-width neural networks.
It provides a high-level API for specifying complex and hierarchical neural network architectures.
These networks can then be trained and evaluated either at finite-width as usual or in their infinite-width limit.
Infinite-width networks can be trained analytically using exact Bayesian inference or using gradient descent via the Neural Tangent Kernel.
Additionally, Neural Tangents provides tools to study gradient descent training dynamics of wide but finite networks in either function space or weight space.
The entire library runs out-of-the-box on CPU, GPU, or TPU.
All computations can be automatically distributed over multiple accelerators with near-linear scaling in the number of devices.
Neural Tangents is available
at
https://www.github.com/google/neural-tangents
We also provide an accompanying interactive Colab notebook at
https://colab.sandbox.google.com/github/google/neural-tangents/blob/master/notebooks/neural_tangents_cookbook.ipynb

assistant: text: Keras for infinite neural networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: JAUNE: Justified And Unified Neural language Evaluation

Paper content:
We review the limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, and introduce JAUNE:  a set of criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.

assistant: text: This paper proposes a new JAUNE metric for the evaluation of machine translation and text summarization systems, showing that their model corresponds better to ground truth similarity labels than BLEU.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play

Paper content:
We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner.
Our scheme pits two versions of the same agent, Alice and Bob, against one another.
Alice proposes a task for Bob to complete; and then Bob attempts to complete the task.
In this work we will focus on two kinds of environments: (nearly) reversible environments and environments that can be reset.
Alice will "propose" the task by doing a sequence of actions and then Bob must undo or repeat them, respectively.
Via an appropriate reward structure, Alice and Bob automatically generate a curriculum of exploration, enabling unsupervised training of the agent.
When Bob is deployed on an RL task within the environment, this unsupervised training reduces the number of supervised episodes needed to learn, and in some cases converges to a higher reward.
Model-free approaches to reinforcement learning are sample inefficient, typically requiring a huge number of episodes to learn a satisfactory policy.
The lack of an explicit environment model means the agent must learn the rules of the environment from scratch at the same time as it tries to understand which trajectories lead to rewards.
In environments where reward is sparse, only a small fraction of the agents' experience is directly used to update the policy, contributing to the inefficiency.In this paper we introduce a novel form of unsupervised training for an agent that enables exploration and learning about the environment without any external reward that incentivizes the agents to learn how to transition between states as efficiently as possible.
We demonstrate that this unsupervised training allows the agent to learn new tasks within the environment quickly.
In this work we described a novel method for intrinsically motivated learning which we call asymmetric self-play.
Despite the method's conceptual simplicity, we have seen that it can be effective in both discrete and continuous input settings with function approximation, for encouraging ex- ploration and automatically generating curriculums.
On the challenging benchmarks we consider, our approach is at least as good as state-of-the-art RL methods that incorporate an incentive for exploration, despite being based on very different principles.
Furthermore, it is possible show theoretically that in simple environments, using asymmetric self-play with reward functions from FORMULA0 and FORMULA1 , optimal agents can transit between any pair of reachable states as efficiently as possible.
Code for our approach can be found at (link removed for anonymity).
A PSEUDO CODE Algorithm 1 and 2 are the pseudo codes for training an agent on self-play and target task episodes.Algorithm 1 Pseudo code for training an agent on a self-play episode DISPLAYFORM0

assistant: text: Unsupervised learning for reinforcement learning using an automatic curriculum of self-play
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mix-review: Alleviate Forgetting in the Pretrain-Finetune Framework for Neural Language Generation Models

Paper content:
In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator.
We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task.
We find that after standard fine-tuning, the model forgets important language generation skills acquired during large-scale pre-training.
We demonstrate the forgetting phenomenon through a detailed behavior analysis from the perspectives of context sensitivity and knowledge transfer.
Adopting the concept of data mixing, we propose an intuitive fine-tuning strategy named "mix-review''.
We find that mix-review effectively regularize the fine-tuning process, and the forgetting problem is largely alleviated.
Finally, we discuss interesting behavior of the resulting dialogue model and its implications.

assistant: text: Study of the forgetting problem in the pretrain-finetune framework, specifically in dialogue response generation tasks, and proposal of a mix-review strategy to alleviate the forgetting issue.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift

Paper content:
Few-Shot Learning (learning with limited labeled data) aims to overcome the limitations of traditional machine learning approaches which require thousands of labeled examples to train an effective model.
Considered as a hallmark of human intelligence, the community has recently witnessed several contributions on this topic, in particular through meta-learning, where a model learns how to learn an effective model for few-shot learning.
The main idea is to acquire prior knowledge from a set of training tasks, which is then used to perform (few-shot) test tasks.
Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.
This is a very strong assumption which restricts the usage of meta-learning strategies in the real world where ample training tasks following the same distribution as test tasks may not be available.
In this paper, we propose a novel meta-learning paradigm wherein a few-shot learning model is learnt, which simultaneously overcomes domain shift between the train and test tasks via adversarial domain adaptation.
We demonstrate the efficacy the proposed method through extensive experiments.

assistant: text: Meta Learning for Few Shot learning assumes that training tasks and test tasks are drawn from the same distribution. What do you do if they are not? Meta Learning with task-level Domain Adaptation!
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Paper content:
Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.
While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.
Current methods rely heavily on on-policy experience, limiting their sample efficiency.
They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.
In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.
In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.
This probabilistic interpretation enables posterior sampling for structured and efficient exploration.
We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.
Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.

assistant: text: Sample efficient meta-RL by combining variational inference of probabilistic task variables with off-policy RL
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Relational State-Space Model for Stochastic Multi-Object Systems

Paper content:
Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other.
Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents.
This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects.
By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics.
We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning.
The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.

assistant: text: The paper presents a relational state-space model that simulates the joint state transitions of correlated objects which are hierarchically coordinated in a graph structure.