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

Title: Learning to Understand Goal Specifications by Modelling Reward

Paper content:
Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards.
However, this places on environment designers the onus of designing language-conditional reward functions which may not be easily or tractably implemented as the complexity of the environment and the language scales.
To overcome this limitation, we present a framework within which instruction-conditional RL agents are trained using rewards obtained not from the environment, but from reward models which are jointly trained from expert examples.
As reward models improve, they learn to accurately reward agents for completing tasks for environment configurations---and for instructions---not present amongst the expert data.
This framework effectively separates the representation of what instructions require from how they can be executed.
In a simple grid world, it enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements.
We further show the method allows our agent to adapt to changes in the environment without requiring new expert examples.

assistant: text: We propose AGILE, a framework for training agents to perform instructions from examples of respective goal-states.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Spatial Broadcast Decoder: A Simple Architecture for Disentangled Representations in VAEs

Paper content:
We present a neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.
Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-“coordinate” channels, and apply a fully convolutional network with 1x1 stride.
This provides an architectural prior for dissociating positional from non-positional features in the latent space, yet without providing any explicit supervision to this effect.
We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space.
We show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.

assistant: text: We introduce a neural rendering architecture that helps VAEs learn disentangled latent representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Storyboarding of Recipes: Grounded Contextual Generation

Paper content:
Information need of humans is essentially multimodal in nature, enabling maximum exploitation of situated context.
We introduce a dataset for sequential procedural (how-to) text generation from images in cooking domain.
The dataset consists of 16,441 cooking recipes with 160,479 photos associated with different steps.
We setup a baseline motivated by the best performing model in terms of human evaluation for the Visual Story Telling (ViST) task.
In addition, we introduce two models to incorporate high level structure learnt by a Finite State Machine (FSM) in neural sequential generation process by: (1) Scaffolding Structure in Decoder (SSiD) (2) Scaffolding Structure in Loss (SSiL).
These models show an improvement in empirical as well as human evaluation.
Our best performing model (SSiL) achieves a METEOR score of 0.31, which is an improvement of 0.6 over the baseline model.
We also conducted human evaluation of the generated grounded recipes, which reveal that 61% found that our proposed (SSiL) model is better than the baseline model in terms of overall recipes, and 72.5% preferred our model in terms of coherence and structure.
We also discuss analysis of the output highlighting key important NLP issues for prospective directions.

assistant: text: The paper presents two techniques to incorporate high level structure in generating procedural text from a sequence of images.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A novel method to determine the number of latent dimensions with SVD

Paper content:
Determining the number of latent dimensions is a ubiquitous problem in machine
learning.
In this study, we introduce a novel method that relies on SVD to discover
the number of latent dimensions.
The general principle behind the method is to
compare the curve of singular values of the SVD decomposition of a data set with
the randomized data set curve.
The inferred number of latent dimensions corresponds
to the crossing point of the two curves.
To evaluate our methodology, we
compare it with competing methods such as Kaisers eigenvalue-greater-than-one
rule (K1), Parallel Analysis (PA), Velicers MAP test (Minimum Average Partial).
We also compare our method with the Silhouette Width (SW) technique which is
used in different clustering methods to determine the optimal number of clusters.
The result on synthetic data shows that the Parallel Analysis and our method have
similar results and more accurate than the other methods, and that our methods is
slightly better result than the Parallel Analysis method for the sparse data sets.

assistant: text: In this study, we introduce a novel method that relies on SVD to discover the number of latent dimensions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SHE2: Stochastic Hamiltonian Exploration and Exploitation for Derivative-Free Optimization

Paper content:
Derivative-free optimization (DFO) using trust region methods is frequently used for machine learning applications, such as (hyper-)parameter optimization without the derivatives of objective functions known.
Inspired by the recent work in continuous-time minimizers, our work models the common trust region methods with the exploration-exploitation using a dynamical system coupling a pair of dynamical processes.
While the first exploration process searches the minimum of the blackbox function through minimizing a time-evolving surrogation function, another exploitation process updates the surrogation function time-to-time using the points traversed by the exploration process.
The efficiency of derivative-free optimization thus depends on ways the two processes couple.
In this paper, we propose a novel dynamical system, namely \ThePrev---\underline{S}tochastic \underline{H}amiltonian \underline{E}xploration and \underline{E}xploitation, that surrogates the subregions of blackbox function using a time-evolving quadratic function, then explores and tracks the minimum of the quadratic functions using a fast-converging Hamiltonian system.
The \ThePrev\ algorithm is later provided as a discrete-time numerical approximation to the system.
To further accelerate optimization, we present \TheName\ that parallelizes multiple \ThePrev\ threads for concurrent exploration and exploitation.
Experiment results based on a wide range of machine learning applications show that \TheName\ outperform a boarder range of derivative-free optimization algorithms with faster convergence speed under the same settings.

assistant: text: a new derivative-free optimization algorithms derived from Nesterov's accelerated gradient methods and Hamiltonian dynamics
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Generative 3D Shape Learning from Natural Images

Paper content:
In this paper we present, to the best of our knowledge, the first method to learn a generative model of 3D shapes from natural images in a fully unsupervised way.
For example, we do not use any ground truth 3D or 2D annotations, stereo video, and ego-motion during the training.
Our approach follows the general strategy of Generative Adversarial Networks, where an image generator network learns to create image samples that are realistic enough to fool a discriminator network into believing that they are natural images.
In contrast, in our approach the image gen- eration is split into 2 stages.
In the first stage a generator network outputs 3D ob- jects.
In the second, a differentiable renderer produces an image of the 3D object from a random viewpoint.
The key observation is that a realistic 3D object should yield a realistic rendering from any plausible viewpoint.
Thus, by randomizing the choice of the viewpoint our proposed training forces the generator network to learn an interpretable 3D representation disentangled from the viewpoint.
In this work, a 3D representation consists of a triangle mesh and a texture map that is used to color the triangle surface by using the UV-mapping technique.
We provide analysis of our learning approach, expose its ambiguities and show how to over- come them.
Experimentally, we demonstrate that our method can learn realistic 3D shapes of faces by using only the natural images of the FFHQ dataset.

assistant: text: We train a generative 3D model of shapes from natural images in an fully unsupervised way.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Simple yet Effective Baseline for Robust Deep Learning with Noisy Labels

Paper content:
Recently deep neural networks have shown their capacity to memorize training data, even with noisy labels, which hurts generalization performance.
To mitigate this issue, we propose a simple but effective method that is robust to noisy labels, even with severe noise.
Our objective involves a variance regularization term that implicitly penalizes the Jacobian norm of the neural network on the whole training set (including the noisy-labeled data), which encourages generalization and prevents overfitting to the corrupted labels.
Experiments on noisy benchmarks demonstrate that our approach achieves state-of-the-art performance with a high tolerance to severe noise.

assistant: text: The paper proposed a simple yet effective baseline for learning with noisy labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: DORA The Explorer: Directed Outreaching Reinforcement Action-Selection

Paper content:
Exploration is a fundamental aspect of Reinforcement Learning, typically implemented using stochastic action-selection.
Exploration, however, can be more efficient if directed toward gaining new world knowledge.
Visit-counters have been proven useful both in practice and in theory for directed exploration.
However, a major limitation of counters is their locality.
While there are a few model-based solutions to this shortcoming, a model-free approach is still missing.
We propose $E$-values, a generalization of counters that can be used to evaluate the propagating exploratory value over state-action trajectories.
We compare our approach to commonly used RL techniques, and show that using $E$-values improves learning and performance over traditional counters.
We also show how our method can be implemented with function approximation to efficiently learn continuous MDPs.
We demonstrate this by showing that our approach surpasses state of the art performance in the Freeway Atari 2600 game.

assistant: text: We propose a generalization of visit-counters that evaluate the propagating exploratory value over trajectories, enabling efficient exploration for model-free RL
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control

Paper content:
Recent progress on physics-based character animation has shown impressive breakthroughs on human motion synthesis, through imitating motion capture data via deep reinforcement learning.
However, results have mostly been demonstrated on imitating a single distinct motion pattern, and do not generalize to interactive tasks that require flexible motion patterns due to varying human-object spatial configurations.
To bridge this gap, we focus on one class of interactive tasks---sitting onto a chair.
We propose a hierarchical reinforcement learning framework which relies on a collection of subtask controllers trained to imitate simple, reusable mocap motions, and a meta controller trained to execute the subtasks properly to complete the main task.
We experimentally demonstrate the strength of our approach over different single level and hierarchical baselines.
We also show that our approach can be applied to motion prediction given an image input.
A video highlight can be found at https://youtu.be/XWU3wzz1ip8/.

assistant: text: Synthesizing human motions on interactive tasks using mocap data and hierarchical RL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Systematic Framework for Natural Perturbations from Videos

Paper content:
We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.
As part of this framework, we construct ImageNet-Vid-Robust, a human-expert--reviewed dataset of 22,668 images grouped into 1,145 sets of perceptually similar images derived from frames in the ImageNet Video Object Detection dataset.
We evaluate a diverse array of classifiers trained on ImageNet, including models trained for robustness, and show a median classification accuracy drop of 16\%.
Additionally, we evaluate the Faster R-CNN and R-FCN models for detection, and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.
Our analysis shows that natural perturbations in the real world are heavily problematic for current CNNs, posing a significant challenge to their deployment in safety-critical environments that require reliable, low-latency predictions.

assistant: text: We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Guiding MCTS with Generalized Policies for Probabilistic Planning

Paper content:
We examine techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems.
The Action Schema Network (ASNet) is a recent contribution to planning that uses deep learning and neural networks to learn generalized policies for probabilistic planning problems.
ASNets are well suited to problems where local knowledge of the environment can be exploited to improve performance, but may fail to generalize to problems they were not trained on.
Monte-Carlo Tree Search (MCTS) is a forward-chaining state space search algorithm for optimal decision making which performs simulations to incrementally build a search tree and estimate the values of each state.
Although MCTS can achieve state-of-the-art results when paired with domain-specific knowledge, without this knowledge, MCTS requires a large number of simulations in order to obtain reliable estimates in the search tree.
By combining ASNets with MCTS, we are able to improve the capability of an ASNet to generalize beyond the distribution of problems it was trained on, as well as enhance the navigation of the search space by MCTS.

assistant: text: Techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning without Memorization

Paper content:
The ability to learn new concepts with small amounts of data is a critical aspect of intelligence that has proven challenging for deep learning methods.
Meta-learning has emerged as a promising technique for leveraging data from previous tasks to enable efficient learning of new tasks.
However, most meta-learning algorithms implicitly require that the meta-training tasks be mutually-exclusive, such that no single model can solve all of the tasks at once.
For example, when creating tasks for few-shot image classification, prior work uses a per-task random assignment of image classes to N-way classification labels.
If this is not done, the meta-learner can ignore the task training data and learn a single model that performs all of the meta-training tasks zero-shot, but does not adapt effectively to new image classes.
This requirement means that the user must take great care in designing the tasks, for example by shuffling labels or removing task identifying information from the inputs.
In some domains, this makes meta-learning entirely inapplicable.
In this paper, we address this challenge by designing a meta-regularization objective using information theory that places precedence on data-driven adaptation.
This causes the meta-learner to decide what must be learned from the task training data and what should be inferred from the task testing input.
By doing so, our algorithm can successfully use data from non-mutually-exclusive tasks to efficiently adapt to novel tasks.
We demonstrate its applicability to both contextual and gradient-based meta-learning algorithms, and apply it in practical settings where applying standard meta-learning has been difficult.
Our approach substantially outperforms standard meta-learning algorithms in these settings.

assistant: text: We identify and formalize the memorization problem in meta-learning and solve this problem with novel meta-regularization method, which greatly expand the domain that meta-learning can be  applicable to and effective on.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs

Paper content:
Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs.
In recent years, a variety of  graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.
However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs.
In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework.
Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique.
Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed.
Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.

assistant: text: We propose a novel tensor based method for graph convolutional networks on dynamic graphs
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Resolving Lexical Ambiguity in English–Japanese Neural Machine Translation

Paper content:
Lexical ambiguity, i.e., the presence of two or more meanings for a single word, is an inherent and challenging problem for machine translation systems.
Even though the use of recurrent neural networks and attention mechanisms are expected to solve this problem, machine translation systems are not always able to correctly translate lexically ambiguous sentences.
In this work, I attempt to resolve the problem of lexical ambiguity in English--Japanese neural machine translation systems by combining a pretrained Bidirectional Encoder Representations from Transformer (BERT) language model that can produce contextualized word embeddings and a Transformer translation model, which is a state-of-the-art architecture for the machine translation task.
These two proposed architectures have been shown to be more effective in translating ambiguous sentences than a vanilla Transformer model and the Google Translate system.
Furthermore, one of the proposed models, the Transformer_BERT-WE, achieves a higher BLEU score compared to the vanilla Transformer model in terms of general translation, which is concrete proof that the use of contextualized word embeddings from BERT can not only solve the problem of lexical ambiguity, but also boost the translation quality in general.

assistant: text: The paper solves a lexical ambiguity problem caused from homonym in neural translation by BERT.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: LIA: Latently Invertible Autoencoder with Adversarial Learning

Paper content:
Deep generative models such as Variational AutoEncoder (VAE) and Generative Adversarial Network (GAN) play an increasingly important role in machine learning and computer vision.
However, there are two fundamental issues hindering their real-world applications: the difficulty of conducting variational inference in VAE and the functional absence of encoding real-world samples in GAN.
In this paper, we propose a novel algorithm named Latently Invertible Autoencoder (LIA) to address the above two issues in one framework.
An invertible network and its inverse mapping are symmetrically embedded in the latent space of VAE.
Thus the partial encoder first transforms the input into feature vectors and then the distribution of these feature vectors is reshaped to fit a prior by the invertible network.
The decoder proceeds in the reverse order of the encoder's composite mappings.
A two-stage stochasticity-free training scheme is designed to train LIA via adversarial learning, in the sense that the decoder of LIA is first trained as a standard GAN with the invertible network and then the partial encoder is learned from an autoencoder by detaching the invertible network from LIA.
Experiments conducted on the FFHQ face dataset and three LSUN datasets validate the effectiveness of LIA for inference and generation.

assistant: text: A new model Latently Invertible Autoencoder is proposed to solve the problem of variational inference in VAE using the invertible network and two-stage adversarial training.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Implicit Autoencoders

Paper content:
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions.
We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoencoder, and derive the learning rules based on maximum-likelihood learning.
Using implicit distributions allows us to learn more expressive posterior and conditional likelihood distributions for the autoencoder.
Learning an expressive conditional likelihood distribution enables the latent code to only capture the abstract and high-level information of the data, while the remaining information is captured by the implicit conditional likelihood distribution.
For example, we show that implicit autoencoders can disentangle the global and local information, and perform deterministic or stochastic reconstructions of the images.
We further show that implicit autoencoders can disentangle discrete underlying factors of variation from the continuous factors in an unsupervised fashion, and perform clustering and semi-supervised learning.

assistant: text: We propose a generative autoencoder that can learn expressive posterior and conditional likelihood distributions using implicit distributions, and train the model using a new formulation of the ELBO.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Federated Optimization for Heterogeneous Networks

Paper content:
Federated learning involves training and effectively combining machine learning models from distributed partitions of data (i.e., tasks) on edge devices, and be naturally viewed as a multi- task learning problem.
While Federated Averaging (FedAvg) is the leading optimization method for training non-convex models in this setting, its behavior is not well understood in realistic federated settings when the devices/tasks are statistically heterogeneous, i.e., where each device collects data in a non-identical fashion.
In this work, we introduce a framework, called FedProx, to tackle statistical heterogeneity.
FedProx encompasses FedAvg as a special case.
We provide convergence guarantees for FedProx through a device dissimilarity assumption.
Our empirical evaluation validates our theoretical analysis and demonstrates the improved robustness and stability of FedProx for learning in heterogeneous networks.

assistant: text: We introduce FedProx, a framework to tackle statistical heterogeneity in federated settings with convergence guarantees and improved robustness and stability.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm

Paper content:
Learning to learn is a powerful paradigm for enabling models to learn from data more effectively and efficiently.
A popular approach to meta-learning is to train a recurrent model to read in a training dataset as input and output the parameters of a learned model, or output predictions for new test inputs.
Alternatively, a more recent approach to meta-learning aims to acquire deep representations that can be effectively fine-tuned, via standard gradient descent, to new tasks.
In this paper, we consider the meta-learning problem from the perspective of universality, formalizing the notion of learning algorithm approximation and comparing the expressive power of the aforementioned recurrent models to the more recent approaches that embed gradient descent into the meta-learner.
In particular, we seek to answer the following question: does deep representation combined with standard gradient descent have sufficient capacity to approximate any learning algorithm?
We find that this is indeed true, and further find, in our experiments, that gradient-based meta-learning consistently leads to learning strategies that generalize more widely compared to those represented by recurrent models.

assistant: text: Deep representations combined with gradient descent can approximate any learning algorithm.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: TuckER: Tensor Factorization for Knowledge Graph Completion

Paper content:
Knowledge graphs are structured representations of real world facts.
However, they typically contain only a small subset of all possible facts.
Link prediction is the task of inferring missing facts based on existing ones.
We propose TuckER, a relatively simple yet powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples.
By using this particular decomposition, parameters are shared between relations, enabling multi-task learning.
TuckER outperforms previous state-of-the-art models across several standard link prediction datasets.

assistant: text: We propose TuckER, a relatively simple but powerful linear model for link prediction in knowledge graphs, based on Tucker decomposition of the binary tensor representation of knowledge graph triples.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Implicit Maximum Likelihood Estimation

Paper content:
Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly.
We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions.
Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite.
We also demonstrate encouraging experimental results.

assistant: text: We develop a new likelihood-free parameter estimation method that is equivalent to maximum likelihood under some conditions
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SEGEN: SAMPLE-ENSEMBLE GENETIC EVOLUTIONARY NETWORK MODEL

Paper content:
Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.
With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data.
Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism.
In this paper, we will introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models.
Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations.
The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much “narrower” and “shallower” architecture.
The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies.
From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts, but has sound theoretic interpretability of the learning process and results.
Extensive experiments have been done on several different real-world benchmark datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the state-of-the-art representation learning models.

assistant: text: We introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.
Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification.
In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size.
Current GCNs also restrict graphs to have at most one edge between any pair of nodes.
To this end, we propose a novel multigraph network that learns from multi-relational graphs.
We explicitly model different types of edges: annotated edges, learned edges with abstract meaning, and hierarchical edges.
We also experiment with different ways to fuse the representations extracted from different edge types.
This restriction is sometimes implied from a dataset, however, we relax this restriction for all kinds of datasets.
We achieve state-of-the-art results on a variety of chemical, social, and vision graph classification benchmarks.

assistant: text: A novel approach to graph classification based on spectral graph convolutional networks and its extension to multigraphs with learnable relations and hierarchical structure. We show state-of-the art results on chemical, social and image datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Modelling Working Memory using Deep Recurrent Reinforcement Learning

Paper content:
In cognitive systems, the role of a working memory is crucial for visual reasoning and decision making.
Tremendous progress has been made in understanding the mechanisms of the human/animal working memory, as well as in formulating different frameworks of artificial neural networks.
In the case of humans, the visual working memory (VWM) task is a standard one in which the subjects are presented with a sequence of images, each of which needs to be identified as to whether it was already seen or not.
Our work is a study of multiple ways to learn a working memory model using recurrent neural networks that learn to remember input images across timesteps.
We train these neural networks to solve the working memory task by training them with a sequence of images in supervised and reinforcement learning settings.
The supervised setting uses image sequences with their corresponding labels.
The reinforcement learning setting is inspired by the popular view in neuroscience that the working memory in the prefrontal cortex is modulated by a dopaminergic mechanism.
We consider the VWM task as an environment that rewards the agent when it remembers past information and penalizes it for forgetting.
We quantitatively estimate the performance of these models on sequences of images from a standard image dataset (CIFAR-100).
Further, we evaluate their ability to remember and recall as they are increasingly trained over episodes.
Based on our analysis, we establish that a gated recurrent neural network model with long short-term memory units trained using reinforcement learning is powerful and more efficient in temporally consolidating the input spatial information.
This work is an initial analysis as a part of our ultimate goal to use artificial neural networks to model the behavior and information processing of the working memory of the brain and to use brain imaging data captured from human subjects during the VWM cognitive task to understand various memory mechanisms of the brain.

assistant: text: LSTMs can more effectively model the working memory if they are learned using reinforcement learning, much like the dopamine system that modulates the memory in the prefrontal cortex
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FusionNet: Fusing via Fully-aware Attention with Application to Machine Comprehension

Paper content:
This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.
First, it puts forward a novel concept of "History of Word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation.
Second, it identifies an attention scoring function that better utilizes the "history of word" concept.
Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer.
We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017).
Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.
Context: The Alpine Rhine is part of the Rhine, a famous European river.
The Alpine Rhine begins in the most western part of the Swiss canton of Graubünden, and later forms the border between Switzerland to the West and Liechtenstein and later Austria to the East.
On the other hand, the Danube separates Romania and Bulgaria.
In this paper, we describe a new deep learning model called FusionNet with its application to machine comprehension.
FusionNet proposes a novel attention mechanism with following three contributions:
1. the concept of history-of-word to build the attention using complete information from the lowest word-level embedding up to the highest semantic-level representation;
2. an attention scoring function to effectively and efficiently utilize history-of-word;
3. a fully-aware multi-level fusion to exploit information layer by layer discriminatingly.
We applied FusionNet to MRC task and experimental results show that FusionNet outperforms existing machine reading models on both the SQuAD dataset and the adversarial SQuAD dataset.
We believe FusionNet is a general and improved attention mechanism and can be applied to many tasks.
Our future work is to study its capability in other NLP problems.

assistant: text: We propose a light-weight enhancement for attention and a neural architecture, FusionNet, to achieve SotA on SQuAD and adversarial SQuAD.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Mind Your Language: Learning Visually Grounded Dialog in a Multi-Agent Setting

Paper content:
The task of visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers.
We posit that requiring agents to adhere to rules of human language while also maximizing information exchange is an ill-posed problem, and observe that humans do not stray from a common language, because they are social creatures and have to communicate with many people everyday, and it is far easier to stick to a common language even at the cost of some efficiency loss.
Using this as inspiration, we propose and evaluate a multi-agent dialog framework where each agent interacts with, and learns from, multiple agents, and show that this results in more relevant and coherent dialog (as judged by human evaluators) without sacrificing task performance (as judged by quantitative metrics).

assistant: text: Social agents learn to talk to each other in natural language towards a goal
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 

Paper content:
We introduce a new procedural dynamic system that can generate a variety of shapes that often appear as curves, but technically, the figures are plots of many points.
We name them spiroplots and show how this new system relates to other procedures or processes that generate figures.
Spiroplots are an extremely simple process but with a surprising visual variety.
We prove some fundamental properties and analyze some instances to see how the geometry or topology of the input determines the generated figures.
We show that some spiroplots have a finite cycle and return to the initial situation, whereas others will produce new points infinitely often.
This paper is accompanied by a JavaScript app that allows anyone to generate spiroplots.

assistant: text: A new, very simple dynamic system is introduced that generates pretty patterns; properties are proved and possibilities are explored
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Topology of deep neural networks

Paper content:
We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%.
The goal is to shed light on two well-known mysteries in deep neural networks:
(i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent;
(ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well.
We performed extensive experiments on persistent homology of a range of point cloud data sets.
The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers.
No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero.
Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers.

assistant: text: We show that neural networks operate by changing topologly of a data set and explore how architectural choices effect this change.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Towards Provable Control for Unknown Linear Dynamical Systems

Paper content:
We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state.
Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program.
This approach eliminates the need to solve the non-convex problem of explicit identification of the system and its latent state, and allows for provable optimality guarantees for the control signal.
We give the first efficient algorithm for finding the optimal control signal with an arbitrary time horizon T, with sample complexity (number of training rollouts) polynomial only in log(T) and other relevant parameters.

assistant: text: Using a novel representation of symmetric linear dynamical systems with a latent state, we formulate optimal control as a convex program, giving the first polynomial-time algorithm that solves optimal control with sample complexity only polylogarithmic in the time horizon.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Implicitly Recurrent CNNs Through Parameter Sharing

Paper content:
We introduce a parameter sharing scheme, in which different layers of a convolutional neural network (CNN) are defined by a learned linear combination of parameter tensors from a global bank of templates.
Restricting the number of templates yields a flexible hybridization of traditional CNNs and recurrent networks.
Compared to traditional CNNs, we demonstrate substantial parameter savings on standard image classification tasks, while maintaining accuracy.
Our simple parameter sharing scheme, though defined via soft weights, in practice often yields trained networks with near strict recurrent structure; with negligible side effects, they convert into networks with actual loops.
Training these networks thus implicitly involves discovery of suitable recurrent architectures.
Though considering only the aspect of recurrent links, our trained networks achieve accuracy competitive with those built using state-of-the-art neural architecture search (NAS) procedures.
Our hybridization of recurrent and convolutional networks may also represent a beneficial architectural bias.
Specifically, on synthetic tasks which are algorithmic in nature, our hybrid networks both train faster and extrapolate better to test examples outside the span of the training set.

assistant: text: We propose a method that enables CNN folding to create recurrent connections
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Co-evolution of language and agents in referential games

Paper content:
Referential games offer a grounded learning environment for neural agents which accounts for the fact that language is functionally used to communicate.
However, they do not take into account a second constraint considered to be fundamental for the shape of human language: that it must be learnable by new language learners and thus has to overcome a transmission bottleneck.
In this work, we insert such a bottleneck in a referential game, by introducing a changing population of agents in which new agents learn by playing with more experienced agents.
We show that mere cultural transmission results in a substantial improvement in language efficiency and communicative success, measured in convergence speed, degree of structure in the emerged languages and within-population consistency of the language.
However, as our core contribution, we show that the optimal situation is to co-evolve language and agents.
When we allow the agent population to evolve through genotypical evolution, we achieve across the board improvements on all considered metrics.
These results stress that for language emergence studies cultural evolution is important, but also the suitability of the architecture itself should be considered.

assistant: text: We enable both the cultural evolution of language and the genetic evolution of agents in a referential game, using a new Language Transmission Engine.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unknown-Aware Deep Neural Network

Paper content:
An important property of image classification systems in the real world is that they both accurately classify objects from target classes (``knowns'') and safely reject unknown objects (``unknowns'') that belong to classes not present in the training data.
Unfortunately, although the strong generalization ability of existing CNNs ensures their accuracy when classifying known objects, it also causes them to often assign an unknown to a target class with high confidence.
As a result, simply using low-confidence detections as a way to detect unknowns does not work well.
In this work, we propose an Unknown-aware Deep Neural Network (UDN for short) to solve this challenging problem.
The key idea of UDN is to enhance existing CNNs to support a product operation that models the product relationship among the features produced by convolutional layers.
This way, missing a single key feature of a target class will greatly reduce the probability of assigning an object to this class.
UDN uses a learned ensemble of these product operations, which allows it to balance the contradictory requirements of accurately classifying known objects and correctly rejecting unknowns.
To further improve the performance of UDN at detecting unknowns, we propose an information-theoretic regularization strategy that incorporates the objective of rejecting unknowns into the learning process of UDN.
We experiment on benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN, adding unknowns by injecting one dataset into another.
Our results demonstrate that UDN significantly outperforms state-of-the-art methods at rejecting unknowns by 25 percentage points improvement in accuracy, while still preserving the classification accuracy.

assistant: text: A CNN architecture that can effective rejects the unknowns in test objects