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

Title: Egocentric Spatial Memory Network

Paper content:
Inspired by neurophysiological discoveries of navigation cells in the mammalian
brain, we introduce the first deep neural network architecture for modeling Egocentric
Spatial Memory (ESM).
It learns to estimate the pose of the agent and
progressively construct top-down 2D global maps from egocentric views in a spatially
extended environment.
During the exploration, our proposed ESM network
model updates belief of the global map based on local observations using a recurrent
neural network.
It also augments the local mapping with a novel external
memory to encode and store latent representations of the visited places based on
their corresponding locations in the egocentric coordinate.
This enables the agents
to perform loop closure and mapping correction.
This work contributes in the
following aspects: first, our proposed ESM network provides an accurate mapping
ability which is vitally important for embodied agents to navigate to goal locations.
In the experiments, we demonstrate the functionalities of the ESM network in
random walks in complicated 3D mazes by comparing with several competitive
baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM)
algorithms.
Secondly, we faithfully hypothesize the functionality and the working
mechanism of navigation cells in the brain.
Comprehensive analysis of our model
suggests the essential role of individual modules in our proposed architecture and
demonstrates efficiency of communications among these modules.
We hope this
work would advance research in the collaboration and communications over both
fields of computer science and computational neuroscience.

assistant: text: first deep neural network for modeling Egocentric Spatial Memory inspired by neurophysiological discoveries of navigation cells in mammalian brain
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Learning with Logged Bandit Feedback

Paper content:
We propose a new output layer for deep neural networks that permits the use of logged contextual bandit feedback for training.
Such contextual bandit feedback can be available in huge quantities (e.g., logs of search engines, recommender systems) at little cost, opening up a path for training deep networks on orders of magnitude more data.
To this effect, we propose a Counterfactual Risk Minimization (CRM) approach for training deep networks using an equivariant empirical risk estimator with variance regularization, BanditNet, and show how the resulting objective can be decomposed in a way that allows Stochastic Gradient Descent (SGD) training.
We empirically demonstrate the effectiveness of the method by showing how deep networks -- ResNets in particular -- can be trained for object recognition without conventionally labeled images.

assistant: text: The paper proposes a new output layer for deep networks that permits the use of logged contextual bandit feedback for training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning deep representations by mutual information estimation and maximization

Paper content:
This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder.
Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation's suitability for downstream tasks.
We further control characteristics of the representation by matching to a prior distribution adversarially.
Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures.
DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.

assistant: text: We learn deep representation by maximizing mutual information, leveraging structure in the objective, and are able to compute with fully supervised classifiers with comparable architectures
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics

Paper content:
Sparsely available data points cause a numerical error on finite differences which hinder to modeling the dynamics of physical systems.
The discretization error becomes even larger when the sparse data are irregularly distributed so that the data defined on an unstructured grid, making it hard to build deep learning models to handle physics-governing observations on the unstructured grid.
In this paper, we propose a novel architecture named Physics-aware Difference Graph Networks (PA-DGN) that exploits neighboring information to learn finite differences inspired by physics equations.
PA-DGN further leverages data-driven end-to-end learning to discover underlying dynamical relations between the spatial and temporal differences in given observations.
We demonstrate the superiority of PA-DGN in the approximation of directional derivatives and the prediction of graph signals on the synthetic data and the real-world climate observations from weather stations.

assistant: text: We propose physics-aware difference graph networks designed to effectively learn spatial differences to modeling sparsely-observed dynamics.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Recurrent Experience Replay in Distributed Reinforcement Learning

Paper content:
Building on the recent successes of distributed training of RL agents, in this paper we investigate the training of RNN-based RL agents from distributed prioritized experience replay.
We study the effects of parameter lag resulting in representational drift and recurrent state staleness and empirically derive an improved training strategy.
Using a single network architecture and fixed set of hyper-parameters, the resulting agent, Recurrent Replay Distributed DQN, quadruples the previous state of the art on Atari-57, and matches the state of the art on DMLab-30.
It is the first agent to exceed human-level performance in 52 of the 57 Atari games.

assistant: text: Investigation on combining recurrent neural networks and experience replay leading to state-of-the-art agent on both Atari-57 and DMLab-30 using single set of hyper-parameters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On Evaluating Explainability Algorithms

Paper content:
A plethora of methods attempting to explain predictions of black-box models have been proposed by the Explainable Artificial Intelligence (XAI) community.
Yet, measuring the quality of the generated explanations is largely unexplored, making quantitative comparisons non-trivial.
In this work, we propose a suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations.
These metrics are computationally inexpensive, do not require model-retraining and can be used across different data modalities.
We evaluate them on common explainers such as Grad-CAM, SmoothGrad, LIME and Integrated Gradients.
Our experiments show that the proposed metrics reflect qualitative observations reported in earlier works.

assistant: text: We propose a suite of metrics that capture desired properties of explainability algorithms and use it to objectively compare and evaluate such methods
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rapid Model Comparison by Amortizing Across Models

Paper content:
Comparing the inferences of diverse candidate models is an essential part of model checking and escaping local optima.
To enable efficient comparison, we introduce an amortized variational inference framework that can perform fast and reliable posterior estimation across models of the same architecture.
Our Any Parameter Encoder (APE) extends the encoder neural network common in amortized inference to take both a data feature vector and a model parameter vector as input.
APE thus reduces posterior inference across unseen data and models to a single forward pass.
In experiments comparing candidate topic models for synthetic data and product reviews, our Any Parameter Encoder yields comparable posteriors to more expensive methods in far less time, especially when the encoder architecture is designed in model-aware fashion.

assistant: text: We develop VAEs where the encoder takes a model parameter vector as input, so we can do rapid inference for many models
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unrolled, model-based networks for lensless imaging

Paper content:
We develop end-to-end learned reconstructions for lensless mask-based cameras, including an experimental system for capturing aligned lensless and lensed images for training.
Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters.
In the middle ground, we present several variations of unrolled alternating direction method of multipliers (ADMM) with varying numbers of learned parameters.
The network structure combines knowledge of the physical imaging model with learned parameters updated from the data, which compensate for artifacts caused by physical approximations.
Our unrolled approach is 20X faster than classic methods and produces better reconstruction quality than both the classic and deep methods on our experimental system.

assistant: text: We improve the reconstruction time and quality on an experimental mask-based lensless imager using an end-to-end learning approach which incorporates knowledge of the imaging model.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: AMUSED: A Multi-Stream Vector Representation Method for Use In Natural Dialogue

Paper content:
The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research.
Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system.
To overcome this problem, we propose an end to end multi-stream deep learning architecture which learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse.
A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighbourhood of the entities from a Knowledge Base (KB).
We benchmark these embeddings on next sentence prediction task and significantly improve upon the existing techniques.
Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.

assistant: text: This paper provides a multi -stream end to end approach to learn unified embeddings for query-response pairs in dialogue systems by leveraging contextual, syntactic, semantic and external information together.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scaling Hierarchical Coreference with Homomorphic Compression

Paper content:
Locality sensitive hashing schemes such as \simhash provide compact representations of multisets from which similarity can be estimated.
However, in certain applications, we need to estimate the similarity of dynamically changing sets.
In this case, we need the representation to be a homomorphism so that the hash of unions and differences of sets can be computed directly from the hashes of operands.
We propose two representations that have this property for cosine similarity (an extension of \simhash and angle-preserving random projections), and make substantial progress on a third representation for Jaccard similarity (an extension of \minhash).
We employ these hashes to compress the sufficient statistics of a conditional random field (CRF) coreference model and study how this compression affects our ability to compute similarities as entities are split and merged during inference.
\cut{We study these hashes in a conditional random field (CRF) hierarchical coreference model in order to compute the similarity of entities as they are merged and split during inference.}
We also provide novel statistical analysis of \simhash to help justify it as an estimator inside a CRF, showing that the bias and variance reduce quickly with the number of bits.
On a problem of author coreference, we find that our \simhash scheme allows scaling the hierarchical coreference algorithm by an order of magnitude without degrading its statistical performance or the model's coreference accuracy, as long as we employ at least 128 or 256 bits.
Angle-preserving random projections further improve the coreference quality, potentially allowing even fewer dimensions to be used.

assistant: text: We employ linear homomorphic compression schemes to represent the sufficient statistics of a conditional random field model of coreference and this allows us to scale inference and improve speed by an order of magnitude.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SEARNN: Training RNNs with global-local losses

Paper content:
We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the "learning to search" (L2S) approach to structured prediction.
RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE).
Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses.
Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance.
Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error.
We first demonstrate improved performance over MLE on two different tasks: OCR and spelling correction.
Then, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes.
This allows us to validate the benefits of our approach on a machine translation task.

assistant: text: We introduce SeaRNN, a novel algorithm for RNN training, inspired by the learning to search approach to structured prediction, in order to avoid the limitations of MLE training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach

Paper content:
Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data.
Nevertheless, these networks often generalize well in practice.
It has also been observed that trained networks can often be ``compressed to much smaller representations.
The purpose of this paper is to connect these two empirical observations.
Our main technical result is a generalization bound for compressed networks based on the compressed size that, combined with off-the-shelf compression algorithms, leads to state-of-the-art generalization guarantees.
In particular, we provide the first non-vacuous generalization guarantees for realistic architectures applied to the ImageNet classification problem.
Additionally, we show that compressibility of models that tend to overfit is limited.
Empirical results show that an increase in overfitting increases the number of bits required to describe a trained network.

assistant: text: We obtain non-vacuous generalization bounds on ImageNet-scale deep neural networks by combining an original PAC-Bayes bound and an off-the-shelf neural network compression method.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Gaussian Prototypical Networks for Few-Shot Learning on Omniglot

Paper content:
We propose a novel architecture for k-shot classification on the Omniglot dataset.
Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks.
Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification.
In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix.
Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights.
We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters.
We report results consistent with state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime on the Omniglot dataset.
We explore artificially down-sampling a fraction of images in the training set, which improves our performance.
Our experiments therefore lead us to hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.

assistant: text: A novel architecture for few-shot classification capable of dealing with uncertainty.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-learning curiosity algorithms

Paper content:
Exploration is a key component of successful reinforcement learning, but optimal approaches are computationally intractable, so researchers have focused on hand-designing mechanisms based on exploration bonuses and intrinsic reward, some inspired by curious behavior in natural systems.
In this work, we propose a strategy for encoding curiosity algorithms as programs in a domain-specific language and searching, during a meta-learning phase, for algorithms that enable RL agents to perform well in new domains.
Our rich language of programs, which can combine neural networks with other building blocks including nearest-neighbor modules and can choose its own loss functions, enables the expression of highly generalizable programs that perform well in domains as disparate as grid navigation with image input, acrobot, lunar lander, ant and hopper.
To  make this approach feasible, we develop several pruning techniques, including learning to predict a program's success based on its syntactic properties.
We demonstrate the effectiveness of the approach empirically, finding curiosity strategies that are similar to those in published literature, as well as novel strategies that are competitive with them and generalize well.

assistant: text: Meta-learning curiosity algorithms by searching through a rich space of programs yields novel mechanisms that generalize across very different reinforcement-learning domains.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: ES-MAML: Simple Hessian-Free Meta Learning

Paper content:
We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).
Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies.
We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement.
Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable.
We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.

assistant: text: We provide a new framework for MAML in the ES/blackbox setting, and show that it allows deterministic and linear policies, better exploration, and non-differentiable adaptation operators.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model-based reinforcement learning for biological sequence design

Paper content:
The ability to design biological structures such as DNA or proteins would have considerable medical and industrial impact.
Doing so presents a challenging black-box optimization problem characterized by the large-batch, low round setting due to the need for labor-intensive wet lab evaluations.
In response, we propose using reinforcement learning (RL) based on proximal-policy optimization (PPO) for biological sequence design.
RL provides a flexible framework for optimization generative sequence models to achieve specific criteria, such as diversity among the high-quality sequences discovered.
We propose a model-based variant of PPO, DyNA-PPO, to improve sample efficiency, where the policy for a new round is trained offline using a simulator fit on functional measurements from prior rounds.
To accommodate the growing number of observations across rounds, the simulator model is automatically selected at each round from a pool of diverse models of varying capacity.
On the tasks of designing DNA transcription factor binding sites, designing antimicrobial proteins, and optimizing the energy of Ising models based on protein structure, we find that DyNA-PPO performs significantly better than existing methods in settings in which modeling is feasible, while still not performing worse in situations in which a reliable model cannot be learned.

assistant: text: We augment model-free policy learning with a sequence-level surrogate reward functions and count-based visitation bonus and demonstrate effectiveness in the large batch, low-round regime seen in designing DNA and protein sequences.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Privacy-aware Adaptive Scheduling for Coalition Operations

Paper content:
Coalition operations are essential for responding to the increasing number of world-wide incidents that require large-scale humanitarian assistance.
Many nations and non-governmental organizations regularly coordinate to address such problems but their cooperation is often impeded by limits on what information they are able to share.
In this paper, we consider the     use of an advanced cryptographic technique called secure multi-party computation to enable coalition members to achieve joint objectives while still meeting privacy requirements
. Our particular focus is on a multi-nation aid delivery scheduling task that involves coordinating when and where various aid provider nations will deliver relief materials after the occurrence of a natural disaster
. Even with the use of secure multi-party computation technology, information about private data can leak
.  We describe how the emerging field of  quantitative information flow can be used to help data owners understand the extent to which private data might become vulnerable as the result of possible or actual scheduling operations, and to enable automated adjustments of the scheduling process to ensure privacy requirements

assistant: text: Privacy can be thought about in the same way as other resources in planning
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generative Adversarial Networks For Data Scarcity Industrial Positron Images With Attention

Paper content:
In the industrial field, the positron annihilation is not affected by complex environment, and the gamma-ray photon penetration is strong, so the nondestructive detection of industrial parts can be realized.
Due to the poor image quality caused by gamma-ray photon scattering, attenuation and short sampling time in positron process, we propose the idea of combining deep learning to generate positron images with good quality and clear details by adversarial nets.
The structure of the paper is as follows: firstly, we encode to get the hidden vectors of medical CT images based on transfer Learning, and use PCA to extract positron image features.
Secondly, we construct a positron image memory based on attention mechanism as a whole input to the adversarial nets which uses medical hidden variables as a query.
Finally, we train the whole model jointly and update the input parameters until convergence.
Experiments have proved the possibility of generating rare positron images for industrial non-destructive testing using countermeasure networks, and good imaging results have been achieved.

assistant: text: adversarial nets, attention mechanism, positron images, data scarcity
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Probabilistic Formulation of Unsupervised Text Style Transfer

Paper content:
We present a deep generative model for unsupervised text style transfer that unifies previously proposed non-generative techniques.
Our probabilistic approach models non-parallel data from two domains as a partially observed parallel corpus.
By hypothesizing a parallel latent sequence that generates each observed sequence, our model learns to transform sequences from one domain to another in a completely unsupervised fashion.
In contrast with traditional generative sequence models (e.g. the HMM), our model makes few assumptions about the data it generates: it uses a recurrent language model as a prior and an encoder-decoder as a transduction distribution.
While computation of marginal data likelihood is intractable in this model class, we show that amortized variational inference admits a practical surrogate.
Further, by drawing connections between our variational objective and other recent unsupervised style transfer and machine translation techniques, we show how our probabilistic view can unify some known non-generative objectives such as backtranslation and adversarial loss.
Finally, we demonstrate the effectiveness of our method on a wide range of unsupervised style transfer tasks, including sentiment transfer, formality transfer, word decipherment, author imitation, and related language translation.
Across all style transfer tasks, our approach yields substantial gains over state-of-the-art non-generative baselines, including the state-of-the-art unsupervised machine translation techniques that our approach generalizes.
Further, we conduct experiments on a standard unsupervised machine translation task and find that our unified approach matches the current state-of-the-art.

assistant: text: We formulate a probabilistic latent sequence model to tackle unsupervised text style transfer, and show its effectiveness across a suite of unsupervised text style transfer tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Role of Embedding Complexity in Domain-invariant Representations

Paper content:
Unsupervised domain adaptation aims to generalize the hypothesis trained in a source domain to an unlabeled target domain.
One popular approach to this problem is to learn domain-invariant embeddings for both domains.
In this work, we study, theoretically and empirically, the effect of the embedding complexity on generalization to the target domain.
In particular, this complexity affects an upper bound on the target risk; this is reflected in experiments, too.
Next, we specify our theoretical framework to multilayer neural networks.
As a result, we develop a strategy that mitigates sensitivity to the embedding complexity, and empirically achieves performance on par with or better than the best layer-dependent complexity tradeoff.

assistant: text: We study the effect of the embedding complexity in learning domain-invariant representations and develop a strategy that mitigates sensitivity to it.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Improving MMD-GAN Training with Repulsive Loss Function

Paper content:
Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget.
This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions.
First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data.
To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.
Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function.
The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets.
Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions.
The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.

assistant: text: Rearranging the terms in maximum mean discrepancy yields a much better loss function for the discriminator of generative adversarial nets
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model Ensemble-Based Intrinsic Reward for Sparse Reward Reinforcement Learning

Paper content:
In this paper, a new intrinsic reward generation method for sparse-reward reinforcement learning is proposed based on an ensemble of dynamics models.
In the proposed method, the mixture of multiple dynamics models is used to approximate the true unknown transition probability, and the intrinsic reward is designed as the minimum of the surprise seen from each dynamics model to the mixture of the dynamics models.
In order to show the effectiveness of the proposed intrinsic reward generation method, a working algorithm is constructed by combining the proposed intrinsic reward generation method with the proximal policy optimization (PPO) algorithm.
Numerical results show that for representative locomotion tasks, the proposed model-ensemble-based intrinsic reward generation method outperforms the previous methods based on a single dynamics model.

assistant: text: For sparse-reward reinforcement learning, the ensemble of multiple dynamics models is used to generate intrinsic reward designed as the minimum of the surprise.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Feature-directed Active Learning for Learning User Preferences

Paper content:
Learning preferences of users over plan traces can be a challenging task given a large number of features and limited queries that we can ask a single user.
Additionally, the preference function itself can be quite convoluted and non-linear.
Our approach uses feature-directed active learning to gather the necessary information about plan trace preferences.
This data is used to train a simple feedforward neural network to learn preferences over the sequential data.
We evaluate the impact of active learning on the number of traces that are needed to train a model that is accurate and interpretable.
This evaluation is done by comparing the aforementioned feedforward network to a more complex neural network model that uses LSTMs and is trained with a larger dataset without active learning.

assistant: text: Learning preferences over plan traces using active learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks

Paper content:
Compression is a key step to deploy large neural networks on resource-constrained platforms.
As a popular compression technique, quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight.
In this paper, we study the representation power of quantized neural networks.
First, we prove the universal approximability of quantized ReLU networks on a wide class of functions.
Then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bit-width of weights for function-independent and function-dependent structures.
Our results reveal that, to attain an approximation error bound of $\epsilon$, the number of weights needed by a quantized network is no more than $\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized network.
This overhead is of much lower order than the lower bound of the number of weights needed for the error bound, supporting the empirical success of various quantization techniques.
To the best of our knowledge, this is the first in-depth study on the complexity bounds of quantized neural networks.

assistant: text: This paper proves the universal  approximability of quantized ReLU neural networks and puts forward the complexity bound given arbitrary error.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Convolutional Neural Networks on Non-uniform Geometrical Signals Using Euclidean Spectral Transformation

Paper content:
Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images).
However, most data signals do not natively exist on a grid, and in the process of being sampled onto a uniform physical grid suffer significant aliasing error and information loss.
Moreover, signals can exist in different topological structures as, for example, points, lines, surfaces and volumes.
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
To this end, we develop mathematical formulations for Non-Uniform Fourier Transforms (NUFT) to directly, and optimally, sample nonuniform data signals of different topologies defined on a simplex mesh into the spectral domain with no spatial sampling error.
The spectral transform is performed in the Euclidean space, which removes the translation ambiguity from works on the graph spectrum.
Our representation has four distinct advantages: (1) the process causes no spatial sampling error during initial sampling, (2) the generality of this approach provides a unified framework for using CNNs to analyze signals of mixed topologies, (3) it allows us to leverage state-of-the-art backbone CNN architectures for effective learning without having to design a particular architecture for a particular data structure in an ad-hoc fashion, and (4) the representation allows weighted meshes where each element has a different weight (i.e., texture) indicating local properties.
We achieve good results on-par with state-of-the-art for 3D shape retrieval task, and new state-of-the-art for point cloud to surface reconstruction task.

assistant: text: We use non-Euclidean Fourier Transformation of shapes defined by a simplicial complex for deep learning, achieving significantly better results than point-based sampling techiques used in current 3D learning literature.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Multi-Agent Communication Through Structured Attentive Reasoning

Paper content:
Learning communication via deep reinforcement learning has recently been shown to be an effective way to solve cooperative multi-agent tasks.
However, learning which communicated information is beneficial for each agent's decision-making remains a challenging task.
In order to address this problem, we introduce a fully differentiable framework for communication and reasoning, enabling agents to solve cooperative tasks in partially-observable environments.
The framework is designed to facilitate explicit reasoning between agents, through a novel memory-based attention network that can learn selectively from its past memories.
The model communicates through a series of reasoning steps that decompose each agent's intentions into learned representations that are used first to compute the relevance of communicated information, and second to extract information from memories given newly received information.
By selectively interacting with new information, the model effectively learns a communication protocol directly, in an end-to-end manner.
We empirically demonstrate the strength of our model in cooperative multi-agent tasks, where inter-agent communication and reasoning over prior information substantially improves performance compared to baselines.

assistant: text: Novel architecture of memory based attention mechanism for multi-agent communication.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FAKE CAN BE REAL IN GANS

Paper content:
In order to alleviate the notorious mode collapse phenomenon in generative adversarial networks (GANs), we propose a novel training method of GANs in which certain fake samples can be reconsidered as real ones during the training process.
This strategy can reduce the gradient value that generator receives in the region where gradient exploding happens.
We show that the theoretical equilibrium between the generators and discriminations actually can be seldom realized in practice.
And this results in an unbalanced generated distribution that deviates from the target one, when fake datepoints overfit to real ones, which explains the non-stability of GANs.
We also prove that, by penalizing the difference between discriminator outputs and considering certain fake datapoints as real for adjacent real and fake sample pairs, gradient exploding can be alleviated.
Accordingly, a modified GAN training method is proposed with a more stable training process and a better generalization.
Experiments on different datasets verify our theoretical analysis.

assistant: text: We propose a novel GAN training method by considering certain fake samples as real to alleviate mode collapse and stabilize training process.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TPO: TREE SEARCH POLICY OPTIMIZATION FOR CONTINUOUS ACTION SPACES

Paper content:
Monte Carlo Tree Search (MCTS) has achieved impressive results on a range of discrete environments, such as Go, Mario and Arcade games, but it has not yet fulfilled its true potential in continuous domains.
In this work, we introduceTPO, a tree search based policy optimization method for continuous environments.
TPO takes a hybrid approach to policy optimization.
Building the MCTS tree in a continuous action space and updating the policy gradient using off-policy MCTS trajectories are non-trivial.
To overcome these challenges, we propose limiting tree search branching factor by drawing only few action samples from the policy distribution and define a new loss function based on the trajectories’ mean and standard deviations.
Our approach led to some non-intuitive findings.
MCTS training generally requires a large number of samples and simulations.
However, we observed that bootstrappingtree search with a pre-trained policy allows us to achieve high quality results with a low MCTS branching factor and few number of simulations.
Without the proposed policy bootstrapping, continuous MCTS would require a much larger branching factor and simulation count, rendering it computationally and prohibitively expensive.
In our experiments, we use PPO as our baseline policy optimization algorithm.
TPO significantly improves the policy on nearly all of our benchmarks.
For example, in complex environments such as Humanoid, we achieve a 2.5×improvement over the baseline algorithm.

assistant: text: We use MCTS to further optimize a bootstrapped policy for continuous action spaces under a policy iteration setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dual-module Inference for Efficient Recurrent Neural Networks

Paper content:
Using Recurrent Neural Networks (RNNs) in sequence modeling tasks is promising in delivering high-quality results but challenging to meet stringent latency requirements because of the memory-bound execution pattern of RNNs.
We propose a big-little dual-module inference to dynamically skip unnecessary memory access and computation to speedup RNN inference.
Leveraging the error-resilient feature of nonlinear activation functions used in RNNs, we propose to use a lightweight little module that approximates the original RNN layer, which is referred to as the big module, to compute activations of the insensitive region that are more error-resilient.
The expensive memory access and computation of the big module can be reduced as the results are only used in the sensitive region.
Our method can reduce the overall memory access by 40% on average and achieve 1.54x to 1.75x speedup on CPU-based server platform with negligible impact on model quality.

assistant: text: We accelerate RNN inference by dynamically reducing redundant memory access using a mixture of accurate and approximate modules.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Nowadays deep learning is one of the main topics in almost every field.
It helped to get amazing results in a great number of tasks.
The main problem is that this kind of learning and consequently neural networks, that can be defined deep, are resource intensive.
They need specialized hardware to perform a computation in a reasonable time.
Unfortunately, it is not sufficient to make deep learning "usable" in real life.
Many tasks are mandatory to be as much as possible real-time.
So it is needed to optimize many components such as code, algorithms, numeric accuracy and hardware, to make them "efficient and usable".
All these optimizations can help us to produce incredibly accurate and fast learning models.
Our work focused on two main tasks that have gained significant attention from researchers, that 11 are automated face detection and emotion recognition.
Since these are computationally intensive 12 tasks, not much has been specifically developed or optimized for embedded platforms.
We show how 31 dataset that has 6 classes FIG2 .
To perform reasonable tests an input image of size 100x100x3 has been used.
As shown in Fig. 4 34 we compared results based on computation time for the pipeline with and without accelerations.
Raspberry needs a computation time that is double the time needed by a Movidius, for example the 36 first needs 150ms per frame against the 70ms of the latter.
We conducted several tests and reported 37 the inference time for each task and for the whole pipeline in TAB0 .

assistant: text: Embedded architecture for deep learning on optimized devices for face detection and emotion recognition
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Black-box Adversarial Attacks with Bayesian Optimization

Paper content:
We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.
We use Bayesian optimization (BO) to specifically
cater to scenarios involving low query budgets to develop query efficient adversarial attacks.
We alleviate the issues surrounding BO in regards to optimizing high dimensional deep learning models by effective dimension upsampling techniques.
Our proposed approach achieves performance comparable to the state of the art black-box adversarial attacks albeit with a much lower average query count.
In particular, in low query budget regimes, our proposed method reduces the query count up to 80% with respect to the state of the art methods.

assistant: text: We show that a relatively simple black-box adversarial attack scheme using Bayesian optimization and dimension upsampling  is preferable to existing methods when the number of available queries is very low.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Targeted Adversarial Examples for Black Box Audio Systems

Paper content:
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems.
Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence.
Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known.
In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task.
We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

assistant: text: We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations

Paper content:
Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online.
We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives.
We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms.
Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage.
Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).

assistant: text: We learn an efficient lossy image codec that can be optimized to facilitate reliable photo manipulation detection at fractional cost in payload/quality and even at low bitrates.