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

Title: Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees

Paper content:
Model-based reinforcement learning (RL) is considered to be a promising approach to reduce the sample complexity that hinders model-free RL.
However, the theoretical understanding of such methods has been rather limited.
This paper introduces a novel algorithmic framework for designing and analyzing model-based RL algorithms with theoretical guarantees.
We design a meta-algorithm with a theoretical guarantee of monotone improvement to a local maximum of the expected reward.
The meta-algorithm iteratively builds a lower bound of the expected reward based on the estimated dynamical model and sample trajectories, and then maximizes the lower bound jointly over the policy and the model.
The framework extends the optimism-in-face-of-uncertainty principle to non-linear dynamical models in a way that requires no explicit uncertainty quantification.
Instantiating our framework with simplification gives a  variant of model-based RL algorithms Stochastic Lower Bounds Optimization (SLBO).
Experiments demonstrate that SLBO achieves the state-of-the-art performance when only 1M or fewer samples are permitted on a range of continuous control benchmark tasks.

assistant: text: We design model-based reinforcement learning algorithms with theoretical guarantees and achieve state-of-the-art results on Mujuco benchmark tasks when one million or fewer samples are permitted.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Imbalanced Classification via Adversarial Minority Over-sampling

Paper content:
In most real-world scenarios, training datasets are highly class-imbalanced, where deep neural networks suffer from generalizing to a balanced testing criterion.
In this paper, we explore a novel yet simple way to alleviate this issue via synthesizing less-frequent classes with adversarial examples of other classes.
Surprisingly, we found this counter-intuitive method can effectively learn generalizable features of minority classes by transferring and leveraging the diversity of the majority information.
Our experimental results on various types of class-imbalanced datasets in image classification and natural language processing show that the proposed method not only improves the generalization of minority classes significantly compared to other re-sampling or re-weighting methods, but also surpasses other methods of state-of-art level for the class-imbalanced classification.

assistant: text: The authors propose to tackle imbalance classification using re-sampling methods, showing that adversarial examples in the minority class would help to train a new model that generalizes better.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Sensitivity of Adversarial Robustness to Input Data Distributions

Paper content:
Neural networks are vulnerable to small adversarial perturbations.
Existing literature largely focused on understanding and mitigating the vulnerability of learned models.
In this paper, we demonstrate an intriguing phenomenon about the most popular robust training method in the literature, adversarial training: Adversarial robustness, unlike clean accuracy, is sensitive to the input data distribution.
Even a semantics-preserving transformations on the input data distribution can cause a significantly different robustness for the adversarial trained model that is both trained and evaluated on the new distribution.
Our discovery of such sensitivity on data distribution is based on a study which disentangles the behaviors of clean accuracy and robust accuracy of the Bayes classifier.
Empirical investigations further confirm our finding.
We construct semantically-identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve comparable clean accuracies on them, but adversarially trained models achieve significantly different robustness accuracies.
This counter-intuitive phenomenon indicates that input data distribution alone can affect the adversarial robustness of trained neural networks, not necessarily the tasks themselves.
Lastly, we discuss the practical implications on evaluating adversarial robustness, and make initial attempts to understand this complex phenomenon.

assistant: text: Robustness performance of PGD trained models are sensitive to semantics-preserving transformation of image datasets, which implies the trickiness of evaluation of robust learning algorithms in practice.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning

Paper content:
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
We propose a methodology for testing for statistical differences in model performances under several replications.
To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.
We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.
We find that the selected implementations exhibit stability across random seed, and repeats.
b ∼ Normal(0, σ 2 I) (1) y = Xβ + Zb + α + .(2)Where β ∈ R P is our slope vector, α ∈ R is the intercept, and ∼ Normal(0, I) is the random 127 noise vector.
To model the clusters, we introduce Zb,where Z is the n × q model matrix for the q- and the dependent variable mean is captured by Xβ + α when we marginalize over all the samples.
The random effects component Zb captures variations in the data, it can be interpreted as an individual 134 deviation from the group-level fixed effect.
In our context, we can write the model as follows:136 metric ijk = (A + α 0j + α 1k ) + βExperiment i + i (3) metric ijk = A + βExperiment i + (α 0j + α 1k + i ) (4) DISPLAYFORM0 Where A is the intercept, β is a vector of parameters and Experiment i is a one hot vector of 137 experiments for the observation i.
We can regroup all the random effects, where alpha 0j is a 138 random effect associated with an observation from a random seed j, and alpha 1k is associated to
an observation from a repeat k. Finally, it is possible to regroup all the nuisance parameters in seeds for a given implementation, except for inherent differences due to parallelism on CPU and 250 GPU.
DISPLAYFORM0
Some implementations:
• call a random number generator at an execution point placed before the episodes data 253 generation, hence changing the state of the random number generator,
• generate the episodes data in advance, others generate it for each episode on the fly: different
states of random number generator are involved in the data generation process,
• start training at different states of the random number generator: random number sequences The trend of large-scale compute-intensive ML experiments has caused concern in the community

assistant: text: We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Toward Understanding Generalization of Over-parameterized Deep ReLU network trained with SGD in Student-teacher Setting

Paper content:
To analyze deep ReLU network, we adopt a student-teacher setting in which an over-parameterized student network learns from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD).
Our contributions are two-fold.
First, we prove that when the gradient is zero (or bounded above by a small constant) at every data point in training, a situation called  \emph{interpolation setting}, there exists many-to-one \emph{alignment} between student and teacher nodes in the lowest layer under mild conditions.
This suggests that generalization in unseen dataset is achievable, even the same condition often leads to zero training error.
Second, analysis of noisy recovery and training dynamics in 2-layer network shows that strong teacher nodes (with large fan-out weights) are learned first and subtle teacher nodes are left unlearned until late stage of training.
As a result, it could take a long time to converge into these small-gradient critical points.
Our analysis shows that over-parameterization plays two roles: (1) it is a necessary condition for alignment to happen at the critical points, and (2) in training dynamics, it helps student nodes cover more teacher nodes with fewer iterations.
Both improve generalization.
Experiments justify our finding.

assistant: text: Study of over-parametrization in student-teacher multilayer ReLU networks, a theoretical part about SGD critical points for the teacher-student setting, and a heuristic and empirical part on dynamics of the SDG algorithm as a function of teacher networks.
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: This paper extends the current attention models from word level to the combination of adjacent words, by applying the models to items made from merged adjacent words.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Generalizing Across Domains via Cross-Gradient Training

Paper content:
We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains.
CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain.
Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training.
In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains.
We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances.
CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each other’s objectives.
This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions.
Empirical evaluation on three different applications where this setting is natural establishes that
(1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and
(2) data augmentation is a more stable and accurate method than domain adversarial training.

assistant: text: This paper proposes a domain generalization approach by domain-dependent data augmentation
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Natural Language Detectors Emerge in Individual Neurons

Paper content:
Although deep convolutional networks have achieved improved performance in many natural language tasks, they have been treated as black boxes because they are difficult to interpret.
Especially, little is known about how they represent language in their intermediate layers.
In an attempt to understand the representations of deep convolutional networks trained on language tasks, we show that individual units are selectively responsive to specific morphemes, words, and phrases, rather than responding to arbitrary and uninterpretable patterns.
In order to quantitatively analyze such intriguing phenomenon, we propose a concept alignment method based on how units respond to replicated text.
We conduct analyses with different architectures on multiple datasets for classification and translation tasks and provide new insights into how deep models understand natural language.

assistant: text: Uses grammatical units of natural language that preserve meanings to show that the units of deep CNNs learned in NLP tasks could act as a natural language concept detector.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Recurrent Convolutions: A Model Compression Point of View

Paper content:
Recurrent convolution (RC) shares the same convolutional kernels and unrolls them multiple times, which is originally proposed to model time-space signals.
We suggest that RC can be viewed as a model compression strategy for deep convolutional neural networks.
RC reduces the redundancy across layers and is complementary to most existing model compression approaches.
However, the performance of an RC network can't match the performance of its corresponding standard one, i.e. with the same depth but independent convolutional kernels.
This reduces the value of RC for model compression.
In this paper, we propose a simple variant which improves RC networks: The batch normalization layers of an RC module are learned independently (not shared) for different unrolling steps.
We provide insights on why this works.
Experiments on CIFAR show that unrolling a convolutional layer several steps can improve the performance, thus indirectly plays a role in model compression.

assistant: text: Recurrent convolution for model compression and a trick for training it, that is learning independent BN layres over steps.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: STRUCTURED ALIGNMENT NETWORKS

Paper content:
Many tasks in natural language processing involve comparing two sentences to compute some notion of relevance, entailment, or similarity.
Typically this comparison is done either at the word level or at the sentence level, with no attempt to leverage the inherent structure of the sentence.
When sentence structure is used for comparison, it is obtained during a non-differentiable pre-processing step, leading to propagation of errors.
We introduce a model of structured alignments between sentences, showing how to compare two sentences by matching their latent structures.
Using a structured attention mechanism, our model matches possible spans in the first sentence to possible spans in the second sentence, simultaneously discovering the tree structure of each sentence and performing a comparison, in a model that is fully differentiable and is trained only on the comparison objective.
We evaluate this model on two sentence comparison tasks: the Stanford natural language inference dataset and the TREC-QA dataset.
We find that comparing spans results in superior performance to comparing words individually, and that the learned trees are consistent with actual linguistic structures.

assistant: text: This paper proposes a model of structured alignments between sentences as a means of comparing sentences by matching their latent structures.
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 molecular generative model that generates molecules via a two-step process that provides synthesis routes of the generated molecules, allowing users to examine the synthetic accessibility of generated compounds.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning

Paper content:
Exploration in environments with sparse rewards is a key challenge for reinforcement learning.
How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy?
We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well.
We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning.
These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards.
We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.

assistant: text: structured exploration in deep reinforcement learning via unsupervised visual abstraction discovery and control
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Factorized Multimodal Representations

Paper content:
Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information.
Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data:
1) models must learn the complex intra-modal and cross-modal interactions for prediction and
2) models must be robust to unexpected missing or noisy modalities during testing.
In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels.
We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors.
Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction.
Modality-specific generative factors are unique for each modality and contain the information required for generating data.
Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets.
Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance.
Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.

assistant: text: This paper presents 'Multimodal Factorization model' that factorizes representations into shared multimodal discriminative factors and modality specific generative factors.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

Paper content:
Understanding the representational power of Deep Neural Networks (DNNs) and how their structural properties (e.g., depth, width, type of activation unit) affect the functions they can compute, has been an important yet challenging question in deep learning and approximation theory.
In a seminal paper, Telgarsky high- lighted the benefits of depth by presenting a family of functions (based on sim- ple triangular waves) for which DNNs achieve zero classification error, whereas shallow networks with fewer than exponentially many nodes incur constant error.
Even though Telgarsky’s work reveals the limitations of shallow neural networks, it doesn’t inform us on why these functions are difficult to represent and in fact he states it as a tantalizing open question to characterize those functions that cannot be well-approximated by smaller depths.
In this work, we point to a new connection between DNNs expressivity and Sharkovsky’s Theorem from dynamical systems, that enables us to characterize the depth-width trade-offs of ReLU networks for representing functions based on the presence of a generalized notion of fixed points, called periodic points (a fixed point is a point of period 1).
Motivated by our observation that the triangle waves used in Telgarsky’s work contain points of period 3 – a period that is special in that it implies chaotic behaviour based on the celebrated result by Li-Yorke – we proceed to give general lower bounds for the width needed to represent periodic functions as a function of the depth.
Technically, the crux of our approach is based on an eigenvalue analysis of the dynamical systems associated with such functions.

assistant: text: Shows how the expressive power of NN depends on its depth and width, furthering the understanding of the benefit of deep nets for representing certain function classes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Contrastive Learning of Structured World Models

Paper content:
A structured understanding of our world in terms of objects, relations, and hierarchies is an important component of human cognition.
Learning such a structured world model from raw sensory data remains a challenge.
As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs).
C-SWMs utilize a contrastive approach for representation learning in environments with compositional structure.
We structure each state embedding as a set of object representations and their relations, modeled by a graph neural network.
This allows objects to be discovered from raw pixel observations without direct supervision as part of the learning process.
We evaluate C-SWMs on compositional environments involving multiple interacting objects that can be manipulated independently by an agent, simple Atari games, and a multi-object physics simulation.
Our experiments demonstrate that C-SWMs can overcome limitations of models based on pixel reconstruction and outperform typical representatives of this model class in highly structured environments, while learning interpretable object-based representations.

assistant: text: The authors overcome the problem of using pixel-based losses in the construction and learning of structured world models by using a contrastive latent space.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Credible Sample Elicitation by Deep Learning, for Deep Learning

Paper content:
It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system).
In the literature, there is a line of studies on eliciting distributional information from self-interested agents who hold a relevant information.
Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice.
This is primarily due to the heavy cognitive loads required for human agents to reason and report this high dimensional information.
Consider the example where we are interested in building an image classifier via first collecting a certain category of  high-dimensional image data.
While classical elicitation results apply to eliciting a complex and generative (and continuous) distribution $p(x)$ for this image data, we are interested in eliciting samples $x_i \sim p(x)$ from agents.
This paper introduces a deep learning aided method to incentivize credible sample contributions from selfish and rational agents.
The challenge to do so is to design an incentive-compatible score function to score each reported sample to induce truthful reports, instead of an arbitrary or even adversarial one.
We show that with accurate estimation of a certain $f$-divergence function we are able to achieve approximate incentive compatibility in eliciting truthful samples.
We then present an efficient estimator with theoretical guarantee via studying the variational forms of $f$-divergence function.
Our work complements the literature of information elicitation via introducing the problem of \emph{sample elicitation}.  We also show a connection between this sample elicitation problem and $f$-GAN, and how this connection can help reconstruct an estimator of the distribution based on collected samples.

assistant: text: This paper proposes a deep learning aided method to elicit credible samples from self-interested agents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Reproducibility and Stability Analysis in Metric-Based Few-Shot Learning

Paper content:
We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
We propose a methodology for testing for statistical differences in model performances under several replications.
To study this specific design, we attempt to reproduce results from three prominent papers: Matching Nets, Prototypical Networks, and TADAM.
We analyze on the miniImagenet dataset on the standard classification task in the 5-ways, 5-shots learning setting at test time.
We find that the selected implementations exhibit stability across random seed, and repeats.

assistant: text: We propose a study of the stability of several few-shot learning algorithms subject to variations in the hyper-parameters and optimization schemes while controlling the random seed.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: I love your chain mail! Making knights smile in a fantasy game world

Paper content:
Dialogue research tends to distinguish between chit-chat and goal-oriented tasks.
While the former is arguably more naturalistic and has a wider use of language, the latter has clearer metrics and a more straightforward learning signal.
Humans effortlessly combine the two, and engage in chit-chat for example with the goal of exchanging information or eliciting a specific response.
Here, we bridge the divide between these two domains in the setting of a rich multi-player text-based fantasy environment where agents and humans engage in both actions and dialogue.
Specifically, we train a goal-oriented model with reinforcement learning via self-play against an imitation-learned chit-chat model with two new approaches: the policy either learns to pick a topic or learns to pick an utterance given the top-k utterances.
We show that both models outperform a strong inverse model baseline and can converse naturally with their dialogue partner in order to achieve goals.

assistant: text: Agents interact (speak, act) and can achieve goals in a rich world with diverse language, bridging the gap between chit-chat and goal-oriented dialogue.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Not-So-Random Features

Paper content:
We propose a principled method for kernel learning, which relies on a Fourier-analytic characterization of translation-invariant or rotation-invariant kernels.
Our method produces a sequence of feature maps, iteratively refining the SVM margin.
We provide rigorous guarantees for optimality and generalization, interpreting our algorithm as online equilibrium-finding dynamics in a certain two-player min-max game.
Evaluations on synthetic and real-world datasets demonstrate scalability and consistent improvements over related random features-based methods.

assistant: text: The authors consider learning directly Fourier representations of shift/translation invariant kernels for machine learning applications with the alignment of the kernel to data as the objective function to optimize.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A General Logic-based Approach for Explanation Generation

Paper content:
In an explanation generation problem, an agent needs to identify and explain the reasons for its decisions to another agent.
Existing work in this area is mostly confined to planning-based systems that use automated planning approaches to solve the problem.
In this paper, we approach this problem from a new perspective, where we propose a general logic-based framework for explanation generation.
In particular, given a knowledge base $KB_1$ that entails a formula $\phi$ and a second knowledge base $KB_2$ that does not entail $\phi$, we seek to identify an explanation $\epsilon$ that is a subset of $KB_1$ such that the union of $KB_2$ and $\epsilon$ entails $\phi$.
We define two types of explanations, model- and proof-theoretic explanations, and use cost functions to reflect preferences between explanations.
Further, we present our algorithm implemented for propositional logic that compute such explanations and empirically evaluate it in random knowledge bases and a planning domain.

assistant: text: A general framework for explanation generation using Logic.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Graph Neural Networks with Noisy Labels

Paper content:
We study the robustness to symmetric label noise of GNNs training procedures.
By combining the nonlinear neural message-passing models (e.g. Graph Isomorphism Networks, GraphSAGE, etc.) with loss correction methods, we present a noise-tolerant approach for the graph classification task.
Our experiments show that test accuracy can be improved under the artificial symmetric noisy setting.

assistant: text: This paper proposes the use of a noise correction loss in the context of graph neural networks to deal with noisy labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: FigureQA: An Annotated Figure Dataset for Visual Reasoning

Paper content:
We introduce FigureQA, a visual reasoning corpus of over one million question-answer pairs grounded in over 100,000 images.
The images are synthetic, scientific-style figures from five classes: line plots, dot-line plots, vertical and horizontal bar graphs, and pie charts.
We formulate our reasoning task by generating questions from 15 templates; questions concern various relationships between plot elements and examine characteristics like the maximum, the minimum, area-under-the-curve, smoothness, and intersection.
To resolve, such questions often require reference to multiple plot elements and synthesis of information distributed spatially throughout a figure.
To facilitate the training of machine learning systems, the corpus also includes side data that can be used to formulate auxiliary objectives.
In particular, we provide the numerical data used to generate each figure as well as bounding-box annotations for all plot elements.
We study the proposed visual reasoning task by training several models, including the recently proposed Relation Network as strong baseline.
Preliminary results indicate that the task poses a significant machine learning challenge.
We envision FigureQA as a first step towards developing models that can intuitively recognize patterns from visual representations of data.

assistant: text: This paper introduces a dataset of templated question answering on figures, involving reasoning about figure elements.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Statestream: A toolbox to explore layerwise-parallel deep neural networks

Paper content:
Building deep neural networks to control autonomous agents which have to interact in real-time with the physical world, such as robots or automotive vehicles, requires a seamless integration of time into a network’s architecture.
The central question of this work is, how the temporal nature of reality should be reflected in the execution of a deep neural network and its components.
Most artificial deep neural networks are partitioned into a directed graph of connected modules or layers and the layers themselves consist of elemental building blocks, such as single units.
For most deep neural networks, all units of a layer are processed synchronously and in parallel, but layers themselves are processed in a sequential manner.
In contrast, all elements of a biological neural network are processed in parallel.
In this paper, we define a class of networks between these two extreme cases.
These networks are executed in a streaming or synchronous layerwise-parallel manner, unlocking the layers of such networks for parallel processing.
Compared to the standard layerwise-sequential deep networks, these new layerwise-parallel networks show a fundamentally different temporal behavior and flow of information, especially for networks with skip or recurrent connections.
We argue that layerwise-parallel deep networks are better suited for future challenges of deep neural network design, such as large functional modularized and/or recurrent architectures as well as networks allocating different network capacities dependent on current stimulus and/or task complexity.
We layout basic properties and discuss major challenges for layerwise-parallel networks.
Additionally, we provide a toolbox to design, train, evaluate, and online-interact with layerwise-parallel networks.

assistant: text: A GPU-accelerated toolbox for parallel neuron updating, written in Theano, that supports different update orders in recurrent networks and networks with connections that skip layers.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unsupervised Learning of Entailment-Vector Word Embeddings

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

assistant: text: We train word embeddings based on entailment instead of similarity, successfully predicting lexical entailment.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Locality and Compositionality in Zero-Shot Learning

Paper content:
In this work we study locality and compositionality in the context of learning representations for Zero Shot Learning (ZSL).
In order to well-isolate the importance of these properties in learned representations, we impose the additional constraint that, differently from most recent work in ZSL, no pre-training on different datasets (e.g. ImageNet) is performed.
The results of our experiment show how locality, in terms of small parts of the input, and compositionality, i.e. how well can the learned representations be expressed as a function of a smaller vocabulary, are both deeply related to generalization and motivate the focus on more local-aware models in future research directions for representation learning.

assistant: text: Proposes evaluation framework for ZSL where the model is not allowed to be pretrained and instead, model parameters are randomly initialized for better understanding of what's happening in ZSL.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A General Logic-based Approach for Explanation Generation

Paper content:
In an explanation generation problem, an agent needs to identify and explain the reasons for its decisions to another agent.
Existing work in this area is mostly confined to planning-based systems that use automated planning approaches to solve the problem.
In this paper, we approach this problem from a new perspective, where we propose a general logic-based framework for explanation generation.
In particular, given a knowledge base $KB_1$ that entails a formula $\phi$ and a second knowledge base $KB_2$ that does not entail $\phi$, we seek to identify an explanation $\epsilon$ that is a subset of $KB_1$ such that the union of $KB_2$ and $\epsilon$ entails $\phi$.
We define two types of explanations, model- and proof-theoretic explanations, and use cost functions to reflect preferences between explanations.
Further, we present our algorithm implemented for propositional logic that compute such explanations and empirically evaluate it in random knowledge bases and a planning domain.

assistant: text: This paper researches explanation generation from a KR point of view and conducts experiments measuring explanation size and runtime on random formulas and formulas from a Blocksworld instance.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks

Paper content:
Deep multitask networks, in which one neural network produces multiple predictive outputs, are more scalable and often better regularized than their single-task counterparts.
Such advantages can potentially lead to gains in both speed and performance, but multitask networks are also difficult to train without finding the right balance between tasks.
We present a novel gradient normalization (GradNorm) technique which automatically balances the multitask loss function by directly tuning the gradients to equalize task training rates.
We show that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting over single networks, static baselines, and other adaptive multitask loss balancing techniques.
GradNorm also matches or surpasses the performance of exhaustive grid search methods, despite only involving a single asymmetry hyperparameter $\alpha$.
Thus, what was once a tedious search process which incurred exponentially more compute for each task added can now be accomplished within a few training runs, irrespective of the number of tasks.
Ultimately, we hope to demonstrate that gradient manipulation affords us great control over the training dynamics of multitask networks and may be one of the keys to unlocking the potential of multitask learning.

assistant: text: This work proposes a dynamic weight update scheme that updates weights for different task losses during training time by making use of the loss ratios of different tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Actionable Representations with Goal Conditioned Policies

Paper content:
Representation learning is a central challenge across a range of machine learning areas.
In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems.
Most prior work on representation learning has focused on generative approaches, learning representations that capture all the underlying factors of variation in the observation space in a more disentangled or well-ordered manner.
In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable".
These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, eliminating the need for explicit reconstruction.
We show how these learned representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks.
We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.

assistant: text: Learning state representations which capture factors necessary for control
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Using effective dimension to analyze feature transformations in deep neural networks

Paper content:
In a typical deep learning approach to a computer vision task, Convolutional Neural Networks (CNNs) are used to extract features at varying levels of abstraction from an image and compress a high dimensional input into a lower dimensional decision space through a series of transformations.
In this paper, we investigate how a class of input images is eventually compressed over the course of these transformations.
In particular, we use singular value decomposition to analyze the relevant variations in feature space.
These variations are formalized as the effective dimension of the embedding.
We consider how the effective dimension varies across layers within class.
We show that across datasets and architectures, the effective dimension of a class increases before decreasing further into the network, suggesting some sort of initial whitening transformation.
Further, the decrease rate of the effective dimension deeper in the network corresponds with training performance of the model.
In this section, we analyze and discuss the implications of our findings.
Further, we propose complementary analyses that would bolster our findings.
In studied examples, neural networks initially spherize embeddings and then collapse dimensionality.
The compression of the dimensionality of feature spaces via transformations on inputs is more dramatic in better-performing networks.6.
Appendix is scaled by factor α, the spectral norm of α * Φ (l) is α * σ max (Φ (l) ).
This is also true in a ReLU network with α > 0.
Such a scaling is achieved while preserving φ (l+1) : DISPLAYFORM0 While Srebro et al. BID10 directly apply the trace-norm to bound the complexity of a completed matrix, we apply spectral normalization in Equation 1 to correct for this scale sensitivity.
Hence, a small effective dimension corresponds to an eccentric feature space regardless of magnitude.

assistant: text: Neural networks that do a good job of classification project points into more spherical shapes before compressing them into fewer dimensions.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: B-Spline CNNs on Lie groups

Paper content:
Group convolutional neural networks (G-CNNs) can be used to improve classical CNNs by equipping them with the geometric structure of groups.
Central in the success of G-CNNs is the lifting of feature maps to higher dimensional disentangled representations, in which data characteristics are effectively learned, geometric data-augmentations are made obsolete, and predictable behavior under geometric transformations (equivariance) is guaranteed via group theory.
Currently, however, the practical implementations of G-CNNs are limited to either discrete groups (that leave the grid intact) or continuous compact groups such as rotations (that enable the use of Fourier theory).
In this paper we lift these limitations and propose a modular framework for the design and implementation of G-CNNs for arbitrary Lie groups.
In our approach the differential structure of Lie groups is used to expand convolution kernels in a generic basis of B-splines that is defined on the Lie algebra.
This leads to a flexible framework that enables localized, atrous, and deformable convolutions in G-CNNs by means of respectively localized, sparse and non-uniform B-spline expansions.
The impact and potential of our approach is studied on two benchmark datasets: cancer detection in histopathology slides (PCam dataset) in which rotation equivariance plays a key role and facial landmark localization (CelebA dataset) in which scale equivariance is important.
In both cases, G-CNN architectures outperform their classical 2D counterparts and the added value of atrous and localized group convolutions is studied in detail.

assistant: text: The paper describes a flexible framework for building CNNs that are equivariant to a large class of transformations groups.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explaining the Mistakes of Neural Networks with Latent Sympathetic Examples

Paper content:
Neural networks make mistakes.
The reason why a mistake is made often remains a mystery.
As such neural networks often are considered a black box.
It would be useful to have a method that can give an explanation that is intuitive to a user as to why an image is misclassified.
In this paper we develop a method for explaining the mistakes of a classifier model by visually showing what must be added to an image such that it is correctly classified.
Our work combines the fields of adversarial examples, generative modeling and a correction technique based on difference target propagation to create an technique that creates explanations of why an image is misclassified.
In this paper we explain our method and demonstrate it on MNIST and CelebA.
This approach could aid in demystifying neural networks for a user.

assistant: text: Aims to better understand the classification of neural networks and explores the latent space of a variational auto encoder and considers the perturbations of the latent space in order to obtain the correct classification.