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

Title: simple_rl: Reproducible Reinforcement Learning in Python

Paper content:
Conducting reinforcement-learning experiments can be a complex and timely process.
A full experimental pipeline will typically consist of a simulation of an environment, an implementation of one or many learning algorithms, a variety of additional components designed to facilitate the agent-environment interplay, and any requisite analysis, plotting, and logging thereof.
In light of this complexity, this paper introduces simple rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.
The goal of simple_rl is to support seamless, reproducible methods for running reinforcement learning experiments.
This paper gives an overview  of the core design philosophy of the package, how it differs from existing libraries, and showcases its central features.

assistant: text: This paper introduces and motivates simple_rl, a new open source library for carrying out reinforcement learning experiments in Python 2 and 3 with a focus on simplicity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ranking Policy Gradient

Paper content:
Sample inefficiency is a long-lasting problem in reinforcement learning (RL).
The state-of-the-art uses action value function to derive policy while it usually involves an extensive search over the state-action space and unstable optimization.
Towards the sample-efficient RL, we propose ranking policy gradient (RPG), a policy gradient method that learns the optimal rank of a set of discrete actions.
To accelerate the learning of policy gradient methods, we establish the equivalence between maximizing the lower bound of return and imitating a near-optimal policy without accessing any oracles.
These results lead to a general off-policy learning framework, which preserves the optimality, reduces variance, and improves the sample-efficiency.
We conduct extensive experiments showing that when consolidating with the off-policy learning framework, RPG substantially reduces the sample complexity, comparing to the state-of-the-art.

assistant: text: This paper proposes to reparameterize the policy using a form of ranking to convert the RL problem into a supervised learning problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Monitoring Navigation Agent via Auxiliary Progress Estimation

Paper content:
The Vision-and-Language Navigation (VLN) task entails an agent following navigational instruction in photo-realistic unknown environments.
This challenging task demands that the agent be aware of which instruction was completed, which instruction is needed next, which way to go, and its navigation progress towards the goal.
In this paper, we introduce a self-monitoring agent with two complementary components: (1) visual-textual co-grounding module to locate the instruction completed in the past, the instruction required for the next action, and the next moving direction from surrounding images and (2) progress monitor to ensure the grounded instruction correctly reflects the navigation progress.
We test our self-monitoring agent on a standard benchmark and analyze our proposed approach through a series of ablation studies that elucidate the contributions of the primary components.
Using our proposed method, we set the new state of the art by a significant margin (8% absolute increase in success rate on the unseen test set).
Code is available at https://github.com/chihyaoma/selfmonitoring-agent.

assistant: text: We propose a self-monitoring agent for the Vision-and-Language Navigation task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes

Paper content:
There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs).
This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP.
In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels.
We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible.
Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical.
As a consequence, translation equivariance, beneficial in finite channel CNNs trained with stochastic gradient descent (SGD), is guaranteed to play no role in the Bayesian treatment of the infinite channel limit - a qualitative difference between the two regimes that is not present in the FCN case.
We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs, suggesting advantages from SGD training compared to fully Bayesian parameter estimation.

assistant: text: The paper establishes a connection between infinite channel Bayesian convolutional neural network and Gaussian processes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING

Paper content:
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.
Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.
In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.
TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.
We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.

assistant: text: This paper proposes a meta-learning framework that leverages unlabeled data by learning the graph-based label propogation in an end-to-end manner.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Neural Outlier Rejection for Self-Supervised Keypoint Learning

Paper content:
Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms.
Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks.
However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators.
We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description.
We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution.
Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.

assistant: text: Learning to extract distinguishable keypoints from a proxy task, outlier rejection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Toward predictive machine learning for active vision

Paper content:
We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective.
Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies.
Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data.
The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression.
Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.

assistant: text: Pros and cons of saccade-based computer vision under a predictive coding perspective
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference

Paper content:
Computations for the softmax function in neural network models are expensive when the number of output classes is large.
This can become a significant issue in both training and inference for such models.
In this paper, we present Doubly Sparse Softmax (DS-Softmax), Sparse Mixture of Sparse of Sparse Experts, to improve the efficiency for softmax inference.
During training, our method learns a two-level class hierarchy by dividing entire output class space into several partially overlapping experts.
Each expert is responsible for a learned subset of the output class space and each output class only belongs to a small number of those experts.
During inference, our method quickly locates the most probable expert to compute small-scale softmax.
Our method is learning-based and requires no knowledge of the output class partition space a priori.
We empirically evaluate our method on several real-world tasks and demonstrate that we can achieve significant computation reductions without loss of performance.

assistant: text: We present doubly sparse softmax, the sparse mixture of sparse of sparse experts, to improve the efficiency for softmax inference through exploiting the two-level overlapping hierarchy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Discriminative Particle Filter Reinforcement Learning for Complex Partial observations

Paper content:
Deep reinforcement learning has succeeded in sophisticated games such as Atari, Go, etc.
Real-world decision making, however, often requires reasoning with partial information extracted from complex visual observations.
This paper presents  Discriminative Particle Filter Reinforcement Learning (DPFRL), a new reinforcement learning framework for partial and complex observations.
DPFRL encodes a differentiable particle filter with learned transition and observation models in a neural network, which allows for reasoning with partial observations over multiple time steps.
While a standard particle filter relies on a generative observation model, DPFRL learns a discriminatively parameterized model that is training directly for decision making.
We show that the discriminative parameterization results in significantly improved performance, especially for tasks with complex visual observations, because it circumvents the difficulty of modelling observations explicitly.
In most cases, DPFRL outperforms state-of-the-art POMDP RL models in Flickering Atari Games, an existing POMDP RL benchmark, and in Natural Flickering Atari Games, a new, more challenging POMDP RL benchmark that we introduce.
We further show that DPFRL performs well for visual navigation with real-world data.

assistant: text: We introduce DPFRL, a framework for reinforcement learning under partial and complex observations with a fully differentiable discriminative particle filter
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Can I trust you more? Model-Agnostic Hierarchical Explanations

Paper content:
Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models.
We propose Mahé, a novel approach to provide Model-Agnostic Hierarchical Explanations of how powerful machine learning models, such as deep neural networks, capture these interactions as either dependent on or free of the context of data instances.
Specifically, Mahé provides context-dependent explanations by a novel local interpretation algorithm that effectively captures any-order interactions, and obtains context-free explanations through generalizing context-dependent interactions to explain global behaviors.
Experimental results show that Mahé obtains improved local interaction interpretations over state-of-the-art methods and successfully provides explanations of interactions that are context-free.

assistant: text: A new framework for context-dependent and context-free explanations of predictions
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Semantic Hierarchy Emerges in the Deep Generative Representations for Scene Synthesis

Paper content:
Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.
In this work, we show that highly-structured semantic hierarchy emerges from the generative representations as the variation factors for synthesizing scenes.
By probing the layer-wise representations with a broad set of visual concepts at different abstraction levels, we are able to quantify the causality between the activations and the semantics occurring in the output image.
Such a quantification identifies the human-understandable variation factors learned by GANs to compose scenes.
The qualitative and quantitative results suggest that the generative representations learned by GAN are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.
Identifying such a set of manipulatable latent semantics facilitates semantic scene manipulation.

assistant: text: We show that highly-structured semantic hierarchy emerges in the deep generative representations as a result for synthesizing scenes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Smooth Kernels Improve Adversarial Robustness and Perceptually-Aligned Gradients

Paper content:
Recent research has shown that CNNs are often overly sensitive to high-frequency textural patterns.
Inspired by the intuition that humans are more sensitive to the lower-frequency (larger-scale) patterns we design a regularization scheme that penalizes large differences between adjacent components within each convolutional kernel.
We apply our regularization onto several popular training methods, demonstrating that the models with the proposed smooth kernels enjoy improved adversarial robustness.
Further, building on recent work establishing connections between adversarial robustness and interpretability, we show that our method appears to give more perceptually-aligned gradients.

assistant: text: We introduce a smoothness regularization for convolutional kernels of CNN that can help improve adversarial robustness and lead to perceptually-aligned gradients
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A unified theory of adaptive stochastic gradient descent as Bayesian filtering

Paper content:
We formulate stochastic gradient descent (SGD) as a novel factorised Bayesian filtering problem, in which each parameter is inferred separately, conditioned on the corresopnding backpropagated gradient.
Inference in this setting naturally gives rise to BRMSprop and BAdam: Bayesian variants of RMSprop and Adam.
Remarkably, the Bayesian approach recovers many features of state-of-the-art adaptive SGD methods, including amongst others root-mean-square normalization, Nesterov acceleration and AdamW.  As such, the Bayesian approach provides one explanation for the empirical effectiveness of state-of-the-art adaptive SGD algorithms.
Empirically comparing BRMSprop and BAdam with naive RMSprop and Adam on MNIST, we find that Bayesian methods have the potential to considerably reduce test loss and classification error.

assistant: text: We formulated SGD as a Bayesian filtering problem, and show that this gives rise to RMSprop, Adam, AdamW, NAG and other features of state-of-the-art adaptive methods
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Study of a Simple, Expressive and Consistent Graph Feature Representation

Paper content:
Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.
To circumvent this problem and learn on graphs, graph feature representation is required.
Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.
While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).
We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.
In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.
To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.
Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.

assistant: text: This work proposed to use Graph Laplacian spectrum to learn graph representation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Emergent Communication through Negotiation

Paper content:
Multi-agent reinforcement learning offers a way to study how communication could emerge in communities of agents needing to solve specific problems.
In this paper, we study the emergence of communication in the negotiation environment, a semi-cooperative model of agent interaction.
We introduce two communication protocols - one grounded in the semantics of the game, and one which is a priori ungrounded.
We show that self-interested agents can use the pre-grounded communication channel to negotiate fairly, but are unable to effectively use the ungrounded, cheap talk channel to do the same.
However, prosocial agents do learn to use cheap talk to find an optimal negotiating strategy, suggesting that cooperation is necessary for language to emerge.
We also study communication behaviour in a setting where one agent interacts with agents in a community with different levels of prosociality and show how agent identifiability can aid negotiation.

assistant: text: This paper explores how agents can learn to communicate to solve a negotiation task and find that prosocial agents are able to learn to ground symbols using RL, but self-interested agents are not.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LEARNING TO PROPAGATE LABELS: TRANSDUCTIVE PROPAGATION NETWORK FOR FEW-SHOT LEARNING

Paper content:
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class.
The recently introduced meta-learning approaches tackle this problem by learning a generic classifier across a large number of multiclass classification tasks and generalizing the model to a new task.
Yet, even with such meta-learning, the low-data problem in the novel classification task still remains.
In this paper, we propose Transductive Propagation Network (TPN), a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
Specifically, we propose to learn to propagate labels from labeled instances to unlabeled test instances, by learning a graph construction module that exploits the manifold structure in the data.
TPN jointly learns both the parameters of feature embedding and the graph construction in an end-to-end manner.
We validate TPN on multiple benchmark datasets, on which it largely outperforms existing few-shot learning approaches and achieves the state-of-the-art results.

assistant: text: We propose a novel meta-learning framework for transductive inference that classifies the entire test set at once to alleviate the low-data problem.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Long Short-Term Memory as a Dynamically Computed Element-wise Weighted Sum

Paper content:
Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.
We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.
We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.
Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

assistant: text: This paper proposes a simplified LSTM variants by removing the non-linearity of content item and output gate
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generating Molecules via Chemical Reactions

Paper content:
Over the last few years exciting work in deep generative models has produced models able to suggest new organic molecules by generating strings, trees, and graphs representing their structure.
While such models are able to generate molecules with desirable properties, their utility in practice is limited due to the difficulty in knowing how to synthesize these molecules.
We therefore propose a new molecule generation model, mirroring a more realistic real-world process, where reactants are selected and combined to form more complex molecules.
More specifically, our generative model proposes a bag of initial reactants (selected from a pool of commercially-available molecules) and uses a reaction model to predict how they react together to generate new molecules.
Modeling the entire process of constructing a molecule during generation offers a number of advantages.
First, we show that such a model has the ability to generate a wide, diverse set of valid and unique molecules due to the useful inductive biases of modeling reactions.
Second, modeling synthesis routes rather than final molecules offers practical advantages to chemists who are not only interested in new molecules but also suggestions on stable and safe synthetic routes.
Third, we demonstrate the capabilities of our model to also solve one-step retrosynthesis problems, predicting a set of reactants that can produce a target product.

assistant: text: A deep generative model for organic molecules that first generates reactant building blocks before combining these using a reaction predictor.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Area Attention

Paper content:
Existing attention mechanisms, are mostly item-based in that a model is trained to attend to individual items in a collection (the memory) where each item has a predefined, fixed granularity, e.g., a character or a word.
Intuitively, an area in the memory consisting of multiple items can be worth attending to as a whole.
We propose area attention: a way to attend to an area of the memory, where each area contains a group of items that are either spatially adjacent when the memory has a 2-dimensional structure, such as images, or temporally adjacent for 1-dimensional memory, such as natural language sentences.
Importantly, the size of an area, i.e., the number of items in an area or the level of aggregation, is dynamically determined via learning, which can vary depending on the learned coherence of the adjacent items.
By giving the model the option to attend to an area of items, instead of only individual items, a model can attend to information with varying granularity.
Area attention can work along multi-head attention for attending to multiple areas in the memory.
We evaluate area attention on two tasks: neural machine translation (both character and token-level) and image captioning, and improve upon strong (state-of-the-art) baselines in all the cases.
These improvements are obtainable with a basic form of area attention that is parameter free.
In addition to proposing the novel concept of area attention, we contribute an efficient way for computing it by leveraging the technique of summed area tables.

assistant: text: The paper presents a novel approach for attentional mechanisms that can benefit a range of tasks such as machine translation and image captioning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Learn with Feedback and Local Plasticity

Paper content:
Developing effective biologically plausible learning rules for deep neural networks is important for advancing connections between deep learning and neuroscience.
To date, local synaptic learning rules like those employed by the brain have failed to match the performance of backpropagation in deep networks.
In this work, we employ meta-learning to discover networks that learn using feedback connections and local, biologically motivated learning rules.
Importantly, the feedback connections are not tied to the feedforward weights, avoiding any biologically implausible weight transport.
It can be shown mathematically that this approach has sufficient expressivity to approximate any online learning algorithm.
Our experiments show that the meta-trained networks effectively use feedback connections to perform online credit assignment in multi-layer architectures.
Moreover, we demonstrate empirically that this model outperforms a state-of-the-art gradient-based meta-learning algorithm for continual learning on regression and classification benchmarks.
This approach represents a step toward biologically plausible learning mechanisms that can not only match gradient descent-based learning, but also overcome its limitations.

assistant: text: Networks that learn with feedback connections and local plasticity rules can be optimized for using meta learning.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tensorized Embedding Layers for Efficient Model Compression

Paper content:
The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing.
However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting.
We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance.
We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.

assistant: text: This paper proposes a low-rank tensor decomposition model to parameterize the embedding matrix in Natural Language Processing (NLP), which compresses the network and sometimes increases test accuracy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

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

assistant: text: A self-play model where one agent learns to propose tasks that are easy for them but difficult for an opponent, creating a moving target of self-play objectives and learning curriculum.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Paper content:
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.
The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.
In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth.
To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training.
We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus.
On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB.
Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile.

assistant: text: This paper proposes additional improvement over gradient dropping to improve communication efficiency
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

Paper content:
A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry.
However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds.
This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned from given semantic contexts.
Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points.
Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations.
Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation.
Further, conditional PointGrow learns a smooth manifold of given images where 3D shape interpolation and arithmetic calculation can be performed inside.

assistant: text: The paper introduces a generative model for point clouds using a pixel RNN-like auto-regressive model and an attention model to handle longer-range interactions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title:  A Projection Operator to Balance Consistency and Complexity in Importance Sampling

Paper content:
Importance sampling (IS) is a standard Monte Carlo (MC) tool to compute information about random variables such as moments or quantiles with unknown distributions.
IS is 
asymptotically consistent as the number of MC samples, and hence deltas (particles) that parameterize the density estimate, go to infinity.
However, retaining infinitely many particles is intractable.
We propose a scheme for only keeping a \emph{finite representative subset} of particles and their augmented importance weights that is \emph{nearly consistent}. To do so in {an online manner}, we approximate importance sampling in two ways.
First, we replace the deltas by kernels, yielding kernel density estimates (KDEs).
Second, we sequentially project KDEs onto nearby lower-dimensional subspaces.
We characterize the asymptotic bias of this scheme as determined by a compression parameter and kernel bandwidth, which yields a tunable tradeoff between consistency and memory.
In experiments,  we observe a favorable tradeoff between memory and accuracy, providing for the first time near-consistent compressions of arbitrary posterior distributions.

assistant: text: We proposed a novel compressed kernelized importance sampling algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Pruning neural networks: is it time to nip it in the bud?

Paper content:
Pruning is a popular technique for compressing a neural network: a large pre-trained network is fine-tuned while connections are successively removed.
However, the value of pruning has largely evaded scrutiny.
In this extended abstract, we examine residual networks obtained through Fisher-pruning and make two interesting observations.
First, when time-constrained, it is better to train a simple, smaller network from scratch than prune a large network.
Second, it is the architectures obtained through the pruning process  --- not the learnt weights --- that prove valuable.
Such architectures are powerful when trained from scratch.
Furthermore, these architectures are easy to approximate without any further pruning: we can prune once and obtain a family of new, scalable network architectures for different memory requirements.

assistant: text: Training small networks beats pruning, but pruning finds good small networks to train that are easy to copy.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Out-of-distribution Detection in Few-shot Classification

Paper content:
In many real-world settings, a learning model must perform few-shot classification: learn to classify examples from unseen classes using only a few labeled examples per class.
Additionally, to be safely deployed, it should have the ability to detect out-of-distribution inputs: examples that do not belong to any of the classes.
While both few-shot classification and out-of-distribution detection are popular topics,
their combination has not been studied.
In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.
Then, we propose two new methods for this task and investigate their performance.
In sum, we establish baseline out-of-distribution detection results using standard metrics on new benchmark datasets and show improved results with our proposed methods.

assistant: text: We quantitatively study out-of-distribution detection in few-shot setting, establish baseline results with ProtoNet, MAML, ABML, and improved upon them.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Effect of top-down connections in Hierarchical Sparse Coding

Paper content:
Hierarchical Sparse Coding (HSC) is a powerful model to efficiently represent multi-dimensional, structured data such as images.
The simplest solution to solve this computationally hard problem is to decompose it into independent layerwise subproblems.
However, neuroscientific evidence would suggest inter-connecting these subproblems as in the Predictive Coding (PC) theory, which adds top-down connections between consecutive layers.
In this study, a new model called Sparse Deep Predictive Coding (SDPC) is introduced to assess the impact of this inter-layer feedback connection.
In particular, the SDPC is compared with a Hierarchical Lasso (Hi-La) network made out of a sequence of Lasso layers.
A 2-layered SDPC and a Hi-La networks are trained on 3 different databases and with different sparsity parameters on each layer.
First, we show that the overall prediction error generated by SDPC is lower thanks to the feedback mechanism as it transfers prediction error between layers.
Second, we demonstrate that the inference stage of the SDPC is faster to converge than for the Hi-La model.
Third, we show that the SDPC also accelerates the learning process.
Finally, the qualitative analysis of both models dictionaries, supported by their activation probability, show that the SDPC features are more generic and informative.

assistant: text: This paper presents a study that compares techniques for Hierarchical Sparse Coding, showing that the top-down term is beneficial in reducing predictive error and can learn faster.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Adversarial Learning Framework for a Persona-based Multi-turn Dialogue Model

Paper content:
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on.
The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator.
We also explore two approaches to accomplish the conditional discriminator: (1) $phredGAN_a$, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) $phredGAN_d$, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance.
To demonstrate the superior performance of phredGAN over the persona SeqSeq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends.
Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation.
We also explore the trade-offs from using either variant of $phredGAN$ on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).

assistant: text: This paper develops an adversarial learning framework for neural conversation models with persona
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Write by Learning the Objective

Paper content:
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models for learning prevalent patterns in natural language.
Yet language generated by RNNs often shows several degenerate characteristics that are uncommon in human language; while fluent, RNN language production can be overly generic, repetitive, and even self-contradictory.
We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the abstract qualities of good generation such as Grice’s Maxims.
In this paper, we introduce a general learning framework that can construct a decoding objective better suited for generation.
Starting with a generatively trained RNN language model, our framework learns to construct a substantially stronger generator by combining several discriminatively trained models that can collectively address the limitations of RNN generation.
Human evaluation demonstrates that text generated by the resulting generator is preferred over  that  of  baselines  by  a  large  margin  and  significantly  enhances  the  overall coherence, style, and information content of the generated text.

assistant: text: We build a stronger natural language generator by discriminatively training scoring functions that rank candidate generations with respect to various qualities of good writing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DASED: A Multi-Domain Dataset for Sound Event Detection Domain Adaptation

Paper content:
In this paper we present the first freely available dataset for the development and evaluation of domain adaptation methods, for the sound event detection task.
The dataset contains 40 log mel-band energies extracted from $100$ different synthetic sound event tracks, with additive noise from nine different acoustic scenes (from indoor, outdoor, and vehicle environments), mixed at six different sound-to-noise ratios, SNRs, (from -12 to -27 dB with a step of -3 dB), and totaling to 5400 (9 * 100 * 6) sound files and a total length of 30 564 minutes.
We provide the dataset as is, the code to re-create the dataset and remix the sound event tracks and the acoustic scenes with different SNRs, and a baseline method that tests the adaptation performance with the proposed dataset and establishes some first results.

assistant: text: The very first freely available domain adaptation dataset for sound event detection.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Automatic Measurement on Etched Structure in Semiconductor Using Deep Learning Approach

Paper content:
The fabrication of semiconductor involves etching process to remove selected areas from wafers.
However, the measurement of etched structure in micro-graph heavily relies on time-consuming manual routines.
Traditional image processing usually demands on large number of annotated data and the performance is still poor.
We treat this challenge as segmentation problem and use deep learning approach to detect masks of objects in etched structure of wafer.
Then, we use simple image processing to carry out automatic measurement on the objects.
We attempt Generative Adversarial Network (GAN) to generate more data to overcome the problem of very limited dataset.
We download 10 SEM (Scanning Electron Microscope) images of 4 types from Internet, based on which we carry out our experiments.
Our deep learning based method demonstrates superiority over image processing approach with  mean accuracy reaching over 96% for the measurements, compared with the ground truth.
To the best of our knowledge, it is the first time that deep learning has been applied in semiconductor industry for automatic measurement.

assistant: text: Using deep learning method to carry out automatic measurement of SEM images in semiconductor industry