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

Title: Cross-Lingual Vision-Language Navigation

Paper content:
Vision-Language Navigation (VLN) is the task where an agent is commanded to navigate in photo-realistic unknown environments with natural language instructions.
Previous research on VLN is primarily conducted on the Room-to-Room (R2R) dataset with only English instructions.
The ultimate goal of VLN, however, is to serve people speaking arbitrary languages.
Towards multilingual VLN with numerous languages, we collect a cross-lingual R2R dataset, which extends the original benchmark with corresponding Chinese instructions.
But it is time-consuming and expensive to collect large-scale human instructions for every existing language.
Based on the newly introduced dataset, we propose a general cross-lingual VLN framework to enable instruction-following navigation for different languages.
We first explore the possibility of building a cross-lingual agent when no training data of the target language is available.
The cross-lingual agent is equipped with a meta-learner to aggregate cross-lingual representations and a visually grounded cross-lingual alignment module to align textual representations of different languages.
Under the zero-shot learning scenario, our model shows competitive results even compared to a model trained with all target language instructions.
In addition, we introduce an adversarial domain adaption loss to improve the transferring ability of our model when given a certain amount of target language data.
Our methods and dataset demonstrate the potentials of building a cross-lingual agent to serve speakers with different languages.

assistant: text: We introduce a new task and dataset on cross-lingual vision-language navigation, and propose a general cross-lingual VLN framework for the task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

Paper content:
We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.
To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles.
We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query.
When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.

assistant: text: We design incremental sequence-to-action parsers for text-to-SQL task and achieve SOTA results. We further improve by using non-deterministic oracles to allow multiple correct action sequences.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Conditional Entropy Bottleneck

Paper content:
We present a new family of objective functions, which we term the Conditional Entropy Bottleneck (CEB).
These objectives are motivated by the Minimum Necessary Information (MNI) criterion.
We demonstrate the application of CEB to classification tasks.
We show that CEB gives: well-calibrated predictions; strong detection of challenging out-of-distribution examples and powerful whitebox adversarial examples; and substantial robustness to those adversaries.
Finally, we report that CEB fails to learn from information-free datasets, providing a possible resolution to the problem of generalization observed in Zhang et al. (2016).

assistant: text: The Conditional Entropy Bottleneck is an information-theoretic objective function for learning optimal representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Decoding Decoders: Finding Optimal Representation Spaces for Unsupervised Similarity Tasks

Paper content:
Experimental evidence indicates that simple models outperform complex deep networks on many unsupervised similarity tasks.
Introducing the concept of an optimal representation space, we provide a simple theoretical resolution to this apparent paradox.
In addition, we present a straightforward procedure that, without any retraining or architectural modifications, allows deep recurrent models to perform equally well (and sometimes better) when compared to shallow models.
To validate our analysis, we conduct a set of consistent empirical evaluations and introduce several new sentence embedding models in the process.
Even though this work is presented within the context of natural language processing, the insights are readily applicable to other domains that rely on distributed representations for transfer tasks.

assistant: text: By introducing the notion of an optimal representation space, we provide a theoretical argument and experimental validation that an unsupervised model for sentences can perform well on both supervised similarity and unsupervised transfer tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Practical lossless compression with latent variables using bits back coding

Paper content:
Deep latent variable models have seen recent success in many data domains.
Lossless compression is an application of these models which, despite having the potential to be highly useful, has yet to be implemented in a practical manner.
We present '`Bits Back with ANS' (BB-ANS), a scheme to perform lossless compression with latent variable models at a near optimal rate.
We demonstrate this scheme by using it to compress the MNIST dataset with a variational auto-encoder model (VAE), achieving compression rates superior to standard methods with only a simple VAE.
Given that the scheme is highly amenable to parallelization, we conclude that with a sufficiently high quality generative model this scheme could be used to achieve substantial improvements in compression rate with acceptable running time.
We make our implementation available open source at https://github.com/bits-back/bits-back .

assistant: text: We do lossless compression of large image datasets using a VAE, beat existing compression algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Bayesian Bandits Showdown:  An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

Paper content:
Recent advances in deep reinforcement learning have made significant strides in performance on applications such as Go and Atari games.
However, developing practical methods to balance exploration and exploitation in complex domains remains largely unsolved.
Thompson Sampling and its extension to reinforcement learning provide an elegant approach to exploration that only requires access to posterior samples of the model.
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
Thus, it is attractive to consider approximate Bayesian neural networks in a Thompson Sampling framework.
To understand the impact of using an approximate posterior on Thompson Sampling, we benchmark well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems.
We found that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario.
In particular, we highlight the challenge of adapting slowly converging uncertainty estimates to the online setting.

assistant: text: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Graph Convolutional Network with Sequential Attention For Goal-Oriented Dialogue Systems

Paper content:
Domain specific goal-oriented dialogue systems typically require modeling three types of inputs, viz.,
(i) the knowledge-base associated with the domain,
(ii) the history of the conversation, which is a sequence of utterances and
(iii) the current utterance for which the response needs to be generated.
While modeling these inputs, current state-of-the-art models such as Mem2Seq typically ignore the rich structure inherent in the knowledge graph and the sentences in the conversation context.
Inspired by the recent success of structure-aware Graph Convolutional Networks (GCNs) for various NLP tasks such as machine translation, semantic role labeling and document dating, we propose a memory augmented GCN for goal-oriented dialogues.
Our model exploits
(i) the entity relation graph in a knowledge-base  and
(ii) the dependency graph associated with an utterance to compute richer representations for words and entities.
Further, we take cognizance of the fact that in certain situations, such as, when the conversation is in a code-mixed language, dependency parsers may not be available.
We show that in such situations we could use the global word co-occurrence graph and use it to enrich the representations of utterances.
We experiment with the modified DSTC2 dataset and its recently released code-mixed versions in four languages and show that our method outperforms existing state-of-the-art methods, using a wide range of evaluation metrics.

assistant: text: We propose a Graph Convolutional Network based encoder-decoder model with sequential attention for goal-oriented dialogue systems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Empirical observations on the instability of aligning word vector spaces with GANs

Paper content:
Unsupervised bilingual dictionary induction (UBDI) is useful for unsupervised machine translation and for cross-lingual transfer of models into low-resource languages.
One approach to UBDI is to align word vector spaces in different languages using Generative adversarial networks (GANs) with linear generators, achieving state-of-the-art performance for several language pairs.
For some pairs, however, GAN-based induction is unstable or completely fails to align the vector spaces.
We focus on cases where linear transformations provably exist, but the performance of GAN-based UBDI depends heavily on the model initialization.
We show that the instability depends on the shape and density of the vector sets, but not on noise; it is the result of local optima, but neither over-parameterization nor changing the batch size or the learning rate consistently reduces instability.
Nevertheless, we can stabilize GAN-based UBDI through best-of-N model selection, based on an unsupervised stopping criterion.

assistant: text: An empirical investigation of GAN-based alignment of word vector spaces, focusing on cases, where linear transformations provably exist, but training is unstable.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Constructive Prediction of the Generalization Error Across Scales

Paper content:
The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks.
Nevertheless, the functional form of this dependency remains elusive.
In this work, we present a functional form which approximates well the generalization error in practice.
Capitalizing on the successful concept of model scaling (e.g., width, depth), we are able to simultaneously construct such a form and specify the exact models which can attain it across model/data scales.
Our construction follows insights obtained from observations conducted over a range of model/data scales, in various model types and datasets, in vision and language tasks.
We show that the form both fits the observations well across scales, and provides accurate predictions from small- to large-scale models and data.

assistant: text: We predict the generalization error and specify the model which attains it across model/data scales.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PIE: Pseudo-Invertible Encoder

Paper content:
We consider the problem of information compression from high dimensional data.
Where many studies consider the problem of compression by non-invertible trans- formations, we emphasize the importance of invertible compression.
We introduce new class of likelihood-based auto encoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders.
We provide the theoretical explanation of their principles.
We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperform WAE and VAE in sharpness of the generated images.

assistant: text: New Class of Autoencoders with pseudo invertible architecture
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Logically-Constrained Neural Fitted Q-iteration

Paper content:
This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property.
LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness.
We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed.
The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP.
The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).

assistant: text: As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

Paper content:
In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota.
One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum.
In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative.
In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap.
In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e. the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions.
Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.
However, when done naively, this randomness will inherently make their actions less informative to others during training.
We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase.
During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates.
By combining this simple intuition with an auxiliary task for state prediction and best practices for multi-agent learning, SAD establishes a new state of the art for 2-5 players on the self-play part of the Hanabi challenge.

assistant: text: We develop Simplified Action Decoder, a simple MARL algorithm that beats previous SOTA on Hanabi by a big margin across 2- to 5-player games.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Diverse Generations using Determinantal Point Processes

Paper content:
Generative models have proven to be an outstanding tool for representing high-dimensional probability distributions and generating realistic looking images.
A fundamental characteristic of generative models is their ability to produce multi-modal outputs.
However, while training, they are often susceptible to mode collapse, which means that the model is limited in mapping the input noise to only a few modes of the true data distribution.
In this paper, we draw inspiration from Determinantal Point Process (DPP) to devise a generative model that alleviates mode collapse while producing higher quality samples.
DPP is an elegant probabilistic measure used to model negative correlations within a subset and hence quantify its diversity.
We use DPP kernel to model the diversity in real data as well as in synthetic data.
Then, we devise a generation penalty term that encourages the generator to synthesize data with a similar diversity to real data.
In contrast to previous state-of-the-art generative models that tend to use additional trainable parameters or complex training paradigms, our method does not change the original training scheme.
Embedded in an adversarial training and variational autoencoder, our  Generative DPP approach shows a consistent resistance to mode-collapse on a wide-variety of synthetic data and natural image datasets including MNIST, CIFAR10, and CelebA, while outperforming state-of-the-art methods for data-efficiency, convergence-time, and generation quality.
Our code will be made publicly available.

assistant: text: The addition of a diversity criterion inspired from DPP in the GAN objective avoids mode collapse and leads to better generations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy

Paper content:
We show that Entropy-SGD (Chaudhari et al., 2017), when viewed as a learning algorithm, optimizes a PAC-Bayes bound on the risk of a Gibbs (posterior) classifier, i.e., a randomized classifier obtained by a risk-sensitive perturbation of the weights of a learned classifier.
Entropy-SGD works by optimizing the bound’s prior, violating the hypothesis of the PAC-Bayes theorem that the prior is chosen independently of the data.
Indeed, available implementations of Entropy-SGD rapidly obtain zero training error on random labels and the same holds of the Gibbs posterior.
In order to obtain a valid generalization bound, we show that an ε-differentially private prior yields a valid PAC-Bayes bound, a straightforward consequence of results connecting generalization with differential privacy.
Using stochastic gradient Langevin dynamics (SGLD) to approximate the well-known exponential release mechanism, we observe that generalization error on MNIST (measured on held out data) falls within the (empirically nonvacuous) bounds computed under the assumption that SGLD produces perfect samples.
In particular, Entropy-SGLD can be configured to yield relatively tight generalization bounds and still fit real labels, although these same settings do not obtain state-of-the-art performance.

assistant: text: We show that Entropy-SGD optimizes the prior of a PAC-Bayes bound, violating the requirement that the prior be independent of data; we use differential privacy to resolve this and improve generalization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data

Paper content:
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech.
Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable.
In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems.
In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.
In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning.
With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks.
We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.

assistant: text: We propose a new DNN architecture for deep learning on tabular data
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using Deep Reinforcement Learning to Generate Rationales for Molecules

Paper content:
Deep learning algorithms are increasingly used in modeling chemical processes.
However, black box predictions without rationales have limited used in practical applications, such as drug design.
To this end, we learn to identify molecular substructures -- rationales -- that are associated with the target chemical property (e.g., toxicity).
The rationales are learned in an unsupervised fashion, requiring no additional information beyond the end-to-end task.
We formulate this problem as a reinforcement learning problem over the molecular graph, parametrized by two convolution networks corresponding to the rationale selection and prediction based on it, where the latter induces the reward function.
We evaluate the approach on two benchmark toxicity datasets.
We demonstrate that our model sustains high performance under the additional constraint that predictions strictly follow the rationales.
Additionally, we validate the extracted rationales through comparison against those described in chemical literature and through synthetic experiments.

assistant: text: We use a reinforcement learning over molecular graphs to generate rationales for interpretable molecular property prediction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatic Goal Generation for Reinforcement Learning Agents

Paper content:
Reinforcement learning (RL) is a powerful technique to train an agent to perform a task.
However, an agent that is trained using RL is only capable of achieving the single task that is specified via its reward function.
Such an approach does not scale well to settings in which an agent needs to perform a diverse set of tasks, such as navigating to varying positions in a room or moving objects to varying locations.
Instead, we propose a method that allows an agent to automatically discover the range of tasks that it is capable of performing in its environment.
We use a generator network to propose tasks for the agent to try to achieve, each task being specified as reaching a certain parametrized subset of the state-space.
The generator network is optimized using adversarial training to produce tasks that are always at the appropriate level of difficulty for the agent.
Our method thus automatically produces a curriculum of tasks for the agent to learn.
We show that, by using this framework, an agent can efficiently and automatically learn to perform a wide set of tasks without requiring any prior knowledge of its environment (Videos and code available at: https://sites.google.com/view/goalgeneration4rl).
Our method can also learn to achieve tasks with sparse rewards, which pose significant challenges for traditional RL methods.

assistant: text: We efficiently solve multi-task problems with an automatic curriculum generation algorithm based on a generative model that tracks the learning agent's performance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
Like much of previous work, we seek to align the learned representations of the source and target domains while preserving discriminability.
The way we accomplish alignment is by learning to perform auxiliary self-supervised task(s) on both domains simultaneously.
Each self-supervised task brings the two domains closer together along the direction relevant to that task.
Training this jointly with the main task classifier on the source domain is shown to successfully generalize to the unlabeled target domain.
The presented objective is straightforward to implement and easy to optimize.
We achieve state-of-the-art results on four out of seven standard benchmarks, and competitive results on segmentation adaptation.
We also demonstrate that our method composes well with another popular pixel-level adaptation method.

assistant: text: We use self-supervision on both domain to align them for unsupervised domain adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data augmentation instead of explicit regularization

Paper content:
Modern deep artificial neural networks have achieved impressive results through models with orders of magnitude more parameters than training examples which control overfitting with the help of regularization.
Regularization can be implicit, as is the case of stochastic gradient descent and parameter sharing in convolutional layers, or explicit.
Explicit regularization techniques, most common forms are weight decay and dropout, have proven successful in terms of improved generalization, but they blindly reduce the effective capacity of the model, introduce sensitive hyper-parameters and require deeper and wider architectures to compensate for the reduced capacity.
In contrast, data augmentation techniques exploit domain knowledge to increase the number of training examples and improve generalization without reducing the effective capacity and without introducing model-dependent parameters, since it is applied on the training data.
In this paper we systematically contrast data augmentation and explicit regularization on three popular architectures and three data sets.
Our results demonstrate that data augmentation alone can achieve the same performance or higher as regularized models and exhibits much higher adaptability to changes in the architecture and the amount of training data.

assistant: text: Deep neural networks trained with data augmentation do not require any other explicit regularization (such as weight decay and dropout) and exhibit greater adaptaibility to changes in the architecture and the amount of training data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatic Inference of Sound Correspondence Patterns Across Multiple Languages

Paper content:
Sound correspondence patterns play a crucial role for linguistic reconstruction.
Linguists
use them to prove language relationship, to reconstruct proto-forms, and for classical
phylogenetic reconstruction based on shared innovations.Cognate words which fail to
conform with expected patterns can further point to various kinds of exceptions in sound
change, such as analogy or assimilation of frequent words.
Here we present an automatic
method for the inference of sound correspondence patterns across multiple languages based
on a network approach.
The core idea is to represent all columns in aligned cognate sets as
nodes in a network with edges representing the degree of compatibility between the nodes.
The task of inferring all compatible correspondence sets can then be handled as the well-known minimum clique cover problem in graph theory, which essentially seeks to split the graph into the smallest number of cliques in which each node is represented by exactly one clique.
The resulting partitions represent all correspondence patterns which can be
inferred for a given dataset.
By excluding those patterns which occur in only a few cognate
sets, the core of regularly recurring sound correspondences can be inferred.
Based on this
idea, the paper presents a method for automatic correspondence pattern recognition, which
is implemented as part of a Python library which supplements the paper.
To illustrate the
usefulness of the method, various tests are presented, and concrete examples of the output
of the method are provided.
In addition to the source code, the study is supplemented by
a short interactive tutorial that illustrates how to use the new method and how to inspect
its results.

assistant: text: The paper describes a new algorithm by which sound correspondence patterns for multiple languages can be inferred.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fault Tolerant Reinforcement Learning via A Markov Game of Control and Stopping

Paper content:
Recently, there has been a surge in interest in safe and robust techniques within reinforcement learning (RL).
Current notions of risk in RL fail to capture the potential for systemic failures such as abrupt stoppages from system failures or surpassing of safety thresholds and the appropriate responsive controls in such instances.
We propose a novel approach to fault-tolerance within RL in which the controller learns a policy can cope with adversarial attacks and random stoppages that lead to failures of the system subcomponents.
The results of the paper also cover fault-tolerant (FT) control so that the controller learns to avoid states that carry risk of system failures.
By demonstrating that the class of problems is represented by a variant of SGs, we prove the existence of a solution which is a unique fixed point equilibrium of the game and characterise the optimal controller behaviour.
We then introduce a value function approximation algorithm that converges to the solution through simulation in unknown environments.

assistant: text: The paper tackles fault-tolerance under random and adversarial stoppages.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning how to explain neural networks: PatternNet and PatternAttribution

Paper content:
DeConvNet, Guided BackProp, LRP, were invented to better understand deep neural networks.
We show that these methods do not produce the theoretically correct explanation for a linear model.
Yet they are used on multi-layer networks with millions of parameters.
This is a cause for concern since linear models are simple neural networks.
We argue that explanation methods for neural nets should work reliably in the limit of  simplicity, the linear models.
Based on our analysis of linear models we propose a generalization that yields two explanation techniques (PatternNet and PatternAttribution) that are theoretically sound for linear models and produce improved explanations for deep networks.

assistant: text: Without learning, it is impossible to explain a machine learning model's decisions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network

Paper content:
Weight pruning has been introduced as an efficient model compression technique.
Even though pruning removes significant amount of weights in a network, memory requirement reduction was limited since conventional sparse matrix formats require significant amount of memory to store index-related information.
Moreover, computations associated with such sparse matrix formats are slow because sequential sparse matrix decoding process does not utilize highly parallel computing systems efficiently.
As an attempt to compress index information while keeping the decoding process parallelizable, Viterbi-based pruning was suggested.
Decoding non-zero weights, however, is still sequential in Viterbi-based pruning.
In this paper, we propose a new sparse matrix format in order to enable a highly parallel decoding process of the entire sparse matrix.
The proposed sparse matrix is constructed by combining pruning and weight quantization.
For the latest RNN models on PTB and WikiText-2 corpus, LSTM parameter storage requirement is compressed 19x using the proposed sparse matrix format compared to the baseline model.
Compressed weight and indices can be reconstructed into a dense matrix fast using Viterbi encoders.
Simulation results show that the proposed scheme can feed parameters to processing elements 20 % to 106 % faster than the case where the dense matrix values directly come from DRAM.

assistant: text: We present a new weight encoding scheme which enables high compression ratio and fast sparse-to-dense matrix conversion.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ACE: Artificial Checkerboard Enhancer to Induce and Evade Adversarial Attacks

Paper content:
The checkerboard phenomenon is one of the well-known visual artifacts in the computer vision field.
The origins and solutions of checkerboard artifacts in the pixel space have been studied for a long time, but their effects on the gradient space have rarely been investigated.
In this paper, we revisit the checkerboard artifacts in the gradient space which turn out to be the weak point of a network architecture.
We explore image-agnostic property of gradient checkerboard artifacts and propose a simple yet effective defense method by utilizing the artifacts.
We introduce our defense module, dubbed Artificial Checkerboard Enhancer (ACE), which induces adversarial attacks on designated pixels.
This enables the model to deflect attacks by shifting only a single pixel in the image with a remarkable defense rate.
We provide extensive experiments to support the effectiveness of our work for various attack scenarios using state-of-the-art attack methods.
Furthermore, we show that ACE is even applicable to large-scale datasets including ImageNet dataset and can be easily transferred to various pretrained networks.

assistant: text: We propose a novel aritificial checkerboard enhancer (ACE) module which guides attacks to a pre-specified pixel space and successfully defends it with a simple padding operation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Nonlinear Differential Equations with external forcing

Paper content:
Key equatorial climate phenomena such as QBO and ENSO have never been adequately explained as deterministic processes.
This in spite of recent research showing growing evidence of predictable behavior.
This study applies the fundamental Laplace tidal equations with simplifying assumptions along the equator — i.e. no Coriolis force and a small angle approximation.
The solutions to the partial differential equations are highly non-linear related to Navier-Stokes and only search approaches can be used to fit to the data.

assistant: text: Analytical Formulation of Equatorial Standing Wave Phenomena: Application to QBO and ENSO
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Strong Baseline Defenses Against Clean-Label Poisoning Attacks

Paper content:
Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy), no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases.
In this work, we propose a set of simple, yet highly-effective defenses against these attacks.
We test our proposed approach against two recently published clean-label poisoning attacks, both of which use the CIFAR-10 dataset.
After reproducing their experiments, we demonstrate that our defenses are able to detect over 99% of poisoning examples in both attacks and remove them without any compromise on model performance.
Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks.

assistant: text: We present effective defenses to clean-label poisoning attacks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Extreme Few-view CT Reconstruction using Deep Inference

Paper content:
Reconstruction of few-view x-ray Computed Tomography (CT) data is a highly ill-posed problem.
It is often used in applications that require low radiation dose in clinical CT, rapid industrial scanning, or fixed-gantry CT.
Existing analytic or iterative algorithms generally produce poorly reconstructed images, severely deteriorated by artifacts and noise, especially when the number of x-ray projections is considerably low.
This paper presents a deep network-driven approach to address extreme few-view CT by incorporating convolutional neural network-based inference into state-of-the-art iterative reconstruction.
The proposed method interprets few-view sinogram data using attention-based deep networks to infer the reconstructed image.
The predicted image is then used as prior knowledge in the iterative algorithm for final reconstruction.
We demonstrate effectiveness of the proposed approach by performing reconstruction experiments on a chest CT dataset.

assistant: text: We present a CNN inference-based reconstruction algorithm to address extremely few-view CT.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Achieving Strong Regularization for Deep Neural Networks

Paper content:
L1 and L2 regularizers are critical tools in machine learning due to their ability to simplify solutions.
However, imposing strong L1 or L2 regularization with gradient descent method easily fails, and this limits the generalization ability of the underlying neural networks.
To understand this phenomenon, we investigate how and why training fails for strong regularization.
Specifically, we examine how gradients change over time for different regularization strengths and provide an analysis why the gradients diminish so fast.
We find that there exists a tolerance level of regularization strength, where the learning completely fails if the regularization strength goes beyond it.
We propose a simple but novel method, Delayed Strong Regularization, in order to moderate the tolerance level.
Experiment results show that our proposed approach indeed achieves strong regularization for both L1 and L2 regularizers and improves both accuracy and sparsity on public data sets.
Our source code is published.

assistant: text: We investigate how and why strong L1/L2 regularization fails and propose a method than can achieve strong regularization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Are You Sure YouWant To Do That? Classification with Interpretable Queries

Paper content:
Classification systems typically act in isolation, meaning they are required to implicitly memorize the characteristics of all candidate classes in order to classify.
The cost of this is increased memory usage and poor sample efficiency.
We propose a model which instead verifies using reference images during the classification process, reducing the burden of memorization.
The model uses iterative non-differentiable queries in order to classify an image.
We demonstrate that such a model is feasible to train and can match baseline accuracy while being more parameter efficient.
However, we show that finding the correct balance between image recognition and verification is essential to pushing the model towards desired behavior, suggesting that a pipeline of recognition followed by verification is a more promising approach towards designing more powerful networks with simpler architectures.

assistant: text: Image classification via iteratively querying for reference image from a candidate class with a RNN and use CNN to compare to the input image
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: UCB EXPLORATION VIA Q-ENSEMBLES

Paper content:
We show how an ensemble of $Q^*$-functions can be leveraged for more effective exploration in deep reinforcement learning.
We build on well established algorithms from the bandit setting, and adapt them to the $Q$-learning setting.
We propose an exploration strategy based on upper-confidence bounds (UCB).
Our experiments show significant gains on the Atari benchmark.

assistant: text: Adapting UCB exploration to ensemble Q-learning improves over prior methods such as Double DQN, A3C+ on Atari benchmark
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalized Label Propagation Methods for Semi-Supervised Learning

Paper content:
The key challenge in semi-supervised learning is how to effectively leverage unlabeled data to improve learning performance.
The classical label propagation method, despite its popularity, has limited modeling capability in that it only exploits graph information for making predictions.
In this paper, we consider label propagation from a graph signal processing perspective and decompose it into three components: signal, filter, and classifier.
By extending the three components, we propose a simple generalized label propagation (GLP) framework for semi-supervised learning.
GLP naturally integrates graph and data feature information, and offers the flexibility of selecting appropriate filters and domain-specific classifiers for different applications.
Interestingly, GLP also provides new insight into the popular graph convolutional network and elucidates its working mechanisms.
Extensive experiments on three citation networks, one knowledge graph, and one image dataset demonstrate the efficiency and effectiveness of GLP.

assistant: text: We extend the classical label propation methods to jointly model graph and feature information from a graph filtering perspective, and show connections to the graph convlutional networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MetaPoison:   Learning to craft adversarial poisoning examples via meta-learning

Paper content:
We consider a new class of \emph{data poisoning} attacks on neural networks, in which the attacker takes control of a model by making small perturbations to a subset of its training data.
We formulate the task of finding poisons as a bi-level optimization problem, which can be solved using methods borrowed from the meta-learning community.
Unlike previous poisoning strategies, the meta-poisoning can poison networks that are trained from scratch using an initialization unknown to the attacker and transfer across hyperparameters.
Further we show that our attacks are more versatile: they can cause misclassification of the target image into an arbitrarily chosen class.
Our results show above 50% attack success rate when poisoning just 3-10% of the training dataset.

assistant: text: Generate corrupted training images that are imperceptible yet change CNN behavior on a target during any new training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Efficient Lifelong Learning with A-GEM

Paper content:
In lifelong learning, the learner is presented with a sequence of tasks, incrementally building a data-driven prior which may be leveraged to speed up learning of a new task.
In this work, we investigate the efficiency of current lifelong approaches, in terms of sample complexity, computational and memory cost.
Towards this end, we first introduce a new and a more realistic evaluation protocol, whereby learners observe each example only once and hyper-parameter selection is done on a small and disjoint set of tasks, which is not used for the actual learning experience and evaluation.
Second, we introduce a new metric measuring how quickly a learner acquires a new skill.
Third, we propose an improved version of GEM (Lopez-Paz & Ranzato, 2017), dubbed Averaged GEM (A-GEM), which enjoys the same or even better performance as GEM, while being almost as computationally and memory efficient as EWC (Kirkpatrick et al., 2016) and other regularization-based methods.
Finally, we show that all algorithms including A-GEM can learn even more quickly if they are provided with task descriptors specifying the classification tasks under consideration.
Our experiments on several standard lifelong learning benchmarks demonstrate that A-GEM has the best trade-off between accuracy and efficiency

assistant: text: An efficient lifelong learning algorithm that provides a better trade-off between accuracy and time/ memory complexity compared to other algorithms.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Selfish Emergent Communication

Paper content:
Current literature in machine learning holds that unaligned, self-interested agents do not learn to use an emergent communication channel.
We introduce a new sender-receiver game to study emergent communication for this spectrum of partially-competitive scenarios and put special care into evaluation.
We find that communication can indeed emerge in partially-competitive scenarios, and we discover three things that are tied to improving it.
First, that selfish communication is proportional to cooperation, and it naturally occurs for situations that are more cooperative than competitive.
Second, that stability and performance are improved by using LOLA (Foerster et al, 2018), especially in more competitive scenarios.
And third, that discrete protocols lend themselves better to learning cooperative communication than continuous ones.

assistant: text: We manage to emerge communication with selfish agents, contrary to the current view in ML
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models

Paper content:
The standard variational lower bounds used to train latent variable models produce biased estimates of most quantities of interest.
We introduce an unbiased estimator of the log marginal likelihood and its gradients for latent variable models based on randomized truncation of infinite series.
If parameterized by an encoder-decoder architecture, the parameters of the encoder can be optimized to minimize its variance of this estimator.
We show that models trained using our estimator give better test-set likelihoods than a standard importance-sampling based approach for the same average computational cost.
This estimator also allows use of latent variable models for tasks where unbiased estimators, rather than marginal likelihood lower bounds, are preferred, such as minimizing reverse KL divergences and estimating score functions.

assistant: text: We create an unbiased estimator for the log probability of latent variable models, extending such models to a larger scope of applications.