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

Title: Graph Spectral Regularization For Neural Network Interpretability

Paper content:
Deep neural networks can learn meaningful representations of data.
However, these representations are hard to interpret.
For example, visualizing a latent layer is generally only possible for at most three dimensions.
Neural networks are able to learn and benefit from much higher dimensional representations but these are not visually interpretable because nodes have arbitrary ordering within a layer.
Here, we utilize the ability of the human observer to identify patterns in structured representations to visualize higher dimensions.
To do so, we propose a class of regularizations we call \textit{Graph Spectral Regularizations} that impose graph-structure on latent layers.
This is achieved by treating activations as signals on a predefined graph and constraining those activations using graph filters, such as low pass and wavelet-like filters.
This framework allows for any kind of graph as well as filter to achieve a wide range of structured regularizations depending on the inference needs of the data.
First, we show a synthetic example that the graph-structured layer can reveal topological features of the data.
Next, we show that a smoothing regularization can impose semantically consistent ordering of nodes when applied to capsule nets.
Further, we show that the graph-structured layer, using wavelet-like spatially localized filters, can form localized receptive fields for improved image and biomedical data interpretation.
In other words, the mapping between latent layer, neurons and the output space becomes clear due to the localization of the activations.
Finally, we show that when structured as a grid, the representations create coherent images that allow for image-processing techniques such as convolutions.

assistant: text: A novel regularizer to impose graph structure upon hidden layers of a Neural Network to improve the interpretability of hidden representations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Data-Driven Discovery of Functional Cell Types that Improve Models of Neural Activity

Paper content:
Computational neuroscience aims to fit reliable models of in vivo neural activity and interpret them as abstract computations.
Recent work has shown that functional diversity of neurons may be limited to that of relatively few cell types; other work has shown that incorporating constraints into artificial neural networks (ANNs) can improve their ability to mimic neural data.
Here we develop an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters.
The resulting models are both more predictive and more interpretable, revealing the contributions of functional cell types to neural computation and ultimately informing the design of future ANNs.

assistant: text: We developed an algorithm that takes as input recordings of neural activity and returns clusters of neurons by cell type and models of neural activity constrained by these clusters.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Explanation  by Progressive  Exaggeration

Paper content:
As machine learning methods see greater adoption and implementation in high stakes applications such
as medical image diagnosis, the need for model interpretability and explanation has become more
critical.
Classical approaches that assess feature importance (eg saliency maps) do not explain how and why a particular region of an image is relevant to the prediction.
We propose
a method that explains the outcome of a classification black-box by gradually exaggerating
the semantic effect of a given class.
Given a query input to a classifier, our method produces a
progressive set of plausible variations of that query, which gradually change the posterior probability
from its original class to its negation.
These counter-factually generated samples preserve features
unrelated to the classification decision, such that a user can employ our method as a ``tuning knob'' to traverse a data manifold while crossing the decision boundary.
Our method is model agnostic and only requires the output value and gradient of the predictor with respect to its input.

assistant: text: A method for explaining the output of black box classification of images, that generates gradual perturbation of outputs in response to gradually perturbed input queries.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SDGM: Sparse Bayesian Classifier Based on a Discriminative Gaussian Mixture Model

Paper content:
In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability.
Nevertheless, it is difficult to determine the number of components.
We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM).
In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.
This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components.
The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner.
Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity.
Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.

assistant: text: A sparse classifier based on a discriminative Gaussian mixture model, which can also be embedded into a neural network.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: YellowFin and the Art of Momentum Tuning

Paper content:
Hyperparameter tuning is one of the most time-consuming workloads in deep learning.
State-of-the-art optimizers, such as AdaGrad, RMSProp and Adam, reduce this labor by adaptively tuning an individual learning rate for each variable.
Recently researchers have shown renewed interest in simpler methods like momentum SGD as they may yield better results.
Motivated by this trend, we ask: can simple adaptive methods, based on SGD perform as well or better?
We revisit the momentum SGD algorithm and show that hand-tuning a single learning rate and momentum makes it competitive with Adam.
We then analyze its robustness to learning rate misspecification and objective curvature variation.
Based on these insights, we design YellowFin, an automatic tuner for momentum and learning rate in SGD.
YellowFin optionally uses a negative-feedback loop to compensate for the momentum dynamics in asynchronous settings on the fly.
We empirically show YellowFin can converge in fewer iterations than Adam on ResNets and LSTMs for image recognition, language modeling and constituency parsing, with a speedup of up to $3.28$x in synchronous and up to $2.69$x in asynchronous settings.

assistant: text: YellowFin is an SGD based optimizer with both momentum and learning rate adaptivity.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning From Noisy Singly-labeled Data

Paper content:
Supervised learning depends on annotated examples, which are taken to be the ground truth.
But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk.
Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem).
Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples.
This raises two fundamental questions: (1) How can we best learn from noisy workers?
(2) How should we allocate our labeling budget to maximize the performance of a classifier?
We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data.
The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality.
Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality.
We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold.
Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.

assistant: text: This paper focuses on the learning-from-crowds problem, where jointly updating the classifier weights and the confusion matrices of workers can help on the estimation problem with rare crowdsourced labels.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Relational Multi-Instance Learning for Concept Annotation from Medical Time Series

Paper content:
Recent advances in computing technology and sensor design have made it easier to collect longitudinal or time series data from patients, resulting in a gigantic amount of available medical data.
Most of the medical time series lack annotations or even when the annotations are available they could be subjective and prone to human errors.
Earlier works have developed natural language processing techniques to extract concept annotations and/or clinical narratives from doctor notes.
However, these approaches are slow and do not use the accompanying medical time series data.
To address this issue, we introduce the problem of concept annotation for the medical time series data, i.e., the task of predicting and localizing medical concepts by using the time series data as input.
We propose Relational Multi-Instance Learning (RMIL) - a deep Multi Instance Learning framework based on recurrent neural networks, which uses pooling functions and attention mechanisms for the concept annotation tasks.
Empirical results on medical datasets show that our proposed models outperform various multi-instance learning models.

assistant: text: Proposes a novel Multiple Instance Learning (MIL) formulation called Relation MIL (RMIL), and discussed a number of its variants with LSTM, Bi-LSTM, S2S, etc. and explores integrating RMIL with various attention mechanisms, and demonstrates its usage on medical concept prediction from time series data.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: The Gaussian Process Prior VAE for Interpretable Latent Dynamics from Pixels

Paper content:
We consider the problem of unsupervised learning of a low dimensional, interpretable, latent state of a video containing a moving object.
The problem of distilling dynamics from pixels has been extensively considered through the lens of graphical/state space models that exploit Markov structure for cheap computation and structured graphical model priors for enforcing interpretability on latent representations.
We take a step towards extending these approaches by discarding the Markov structure; instead, repurposing the recently proposed Gaussian Process Prior Variational Autoencoder for learning sophisticated latent trajectories.
We describe the model and perform experiments on a synthetic dataset and see that the model reliably reconstructs smooth dynamics exhibiting U-turns and loops.
We also observe that this model may be trained without any beta-annealing or freeze-thaw of training parameters.
Training is performed purely end-to-end on the unmodified evidence lower bound objective.
This is in contrast to previous works, albeit for slightly different use cases, where application specific training tricks are often required.

assistant: text: We learn sohpisticated trajectories of an object purely from pixels with a toy video dataset by using a VAE structure with a Gaussian process prior.
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 studies the sample elicitation problem, proposing a deep learning approach that relies on the dual expression of the f-divergence which writes as a maximum over a set of functions t.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Melody Generation for Pop Music via Word Representation of Musical Properties

Paper content:
Automatic melody generation for pop music has been a long-time aspiration for
both AI researchers and musicians.
However, learning to generate euphonious
melody has turned out to be highly challenging due to a number of factors.
Representation
of multivariate property of notes has been one of the primary challenges.
It is also difficult to remain in the permissible spectrum of musical variety, outside
of which would be perceived as a plain random play without auditory pleasantness.
Observing the conventional structure of pop music poses further challenges.
In this paper, we propose to represent each note and its properties as a unique
‘word,’ thus lessening the prospect of misalignments between the properties, as
well as reducing the complexity of learning.
We also enforce regularization policies
on the range of notes, thus encouraging the generated melody to stay close
to what humans would find easy to follow.
Furthermore, we generate melody
conditioned on song part information, thus replicating the overall structure of a
full song.
Experimental results demonstrate that our model can generate auditorily
pleasant songs that are more indistinguishable from human-written ones than
previous models.

assistant: text: We propose a novel model to represent notes and their properties, which can enhance the automatic melody generation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Composition-based Multi-Relational Graph Convolutional Networks

Paper content:
Graph Convolutional Networks (GCNs) have recently been shown to be quite successful in modeling graph-structured data.
However, the primary focus has been on handling simple undirected graphs.
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Most of the existing approaches to handle such graphs suffer from over-parameterization and are restricted to learning representations of nodes only.
In this paper, we propose CompGCN, a novel Graph Convolutional framework which jointly embeds both nodes and relations in a relational graph.
CompGCN leverages a variety of entity-relation composition operations from Knowledge Graph Embedding techniques and scales with the number of relations.
It also generalizes several of the existing multi-relational GCN methods.
We evaluate our proposed method on multiple tasks such as node classification, link prediction, and graph classification, and achieve demonstrably superior results.
We make the source code of CompGCN available to foster reproducible research.

assistant: text: The authors develop GCN on multi-relational graphs and propose CompGCN, which leverages insights from knowledge graph embeddings and learns node and relation representations to alleviate the problem of over-parameterization.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: 3D-SIC: 3D Semantic Instance Completion for RGB-D Scans

Paper content:
This paper introduces the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances comprising the scene and infer their complete object geometry.
This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts.
This will open up new possibilities for interactions with object in a scene, for instance for virtual or robotic agents.
To address this task, we propose 3D-SIC, a new data-driven approach that jointly detects object instances and predicts their completed geometry.
The core idea of 3D-SIC is a novel end-to-end 3D neural network architecture that leverages joint color and geometry feature learning.
The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass.
In a series evaluation, we evaluate on both real and synthetic scan benchmark data, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.

assistant: text: Proposes an end-to-end 3D CNN structure which combines color features and 3D features to predict the missing 3D structure of a scene from RGB-D scans.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: POLYNOMIAL ACTIVATION FUNCTIONS

Paper content:
Activation is a nonlinearity function that plays a predominant role in the convergence and performance of deep neural networks.
While Rectified Linear Unit (ReLU) is the most successful activation function, its derivatives have shown superior performance on benchmark datasets.
In this work, we explore the polynomials as activation functions (order ≥ 2) that can approximate continuous real valued function within a given interval.
Leveraging this property, the main idea is to learn the nonlinearity, accepting that the ensuing function may not be monotonic.
While having the ability to learn more suitable nonlinearity, we cannot ignore the fact that it is a challenge to achieve stable performance due to exploding gradients - which is prominent with the increase in order.
To handle this issue, we introduce dynamic input scaling, output scaling, and lower learning rate for the polynomial weights.
Moreover, lower learning rate will control the abrupt fluctuations of the polynomials between weight updates.
In experiments on three public datasets, our proposed method matches the performance of prior activation functions, thus providing insight into a network’s nonlinearity preference.

assistant: text: The authors introduce learnable activation functions that are parameterized by polynomial functions and show results slightly better than ReLU.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Low-bit quantization and quantization-aware training for small-footprint keyword spotting

Paper content:
We investigate low-bit quantization to reduce computational cost of deep neural network (DNN) based keyword spotting (KWS).
We propose approaches to further reduce quantization bits via integrating quantization into keyword spotting model training, which we refer to as quantization-aware training.
Our experimental results on large dataset indicate that quantization-aware training can recover performance models quantized to lower bits representations.
By combining quantization-aware training and weight matrix factorization, we are able to significantly reduce model size and computation for small-footprint keyword spotting, while maintaining performance.

assistant: text: We investigate quantization-aware training in very low-bit quantized keyword spotters to reduce the cost of on-device keyword spotting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stable Recurrent Models

Paper content:
Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks.
In this work, we conduct a thorough investigation of stable recurrent models.
Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent.
Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks.
Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime.
Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.

assistant: text: Stable recurrent models can be approximated by feed-forward networks and empirically perform as well as unstable models on benchmark tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Multiplicative Interactions and Where to Find Them

Paper content:
We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others.
Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated.
We begin by showing that such layers strictly enrich the representable function classes of neural networks.
We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required.
We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation.
Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.

assistant: text: The paper explores different types of multiplicative interactions and finds MI models able to achieve a state-of-the-art performance on language modeling and reinforcement learning problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Visual Imitation with a Minimal Adversary

Paper content:
High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.
In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?
We show that adversarial imitation can work well even in this high dimensional observation space.
Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.
Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.
The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.
Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.
Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

assistant: text: This paper aims at solving the problem of estimating sparse rewards in a high-dimensional input setting.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Paper content:
We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge.
In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions.
We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables).
We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables).
We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs.
Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning.
Experiments in the two-variable case validate the proposed ideas and theoretical results.

assistant: text: The paper shows that a model with the correct underlying structure will adapt faster to a causal intervention than a model with the incorrect structure.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Empirical Study of Encoders and Decoders in Graph-Based Dependency Parsing

Paper content:
Graph-based dependency parsing consists of two steps: first, an encoder produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a decoder} finds the parse tree whose substructures have the largest total score.
Over the past few years, powerful neural techniques have been introduced into the encoding step which substantially increases parsing accuracies.
However, advanced decoding techniques, in particular high-order decoding, have seen a decline in usage.
It is widely believed that contextualized features produced by neural encoders can help capture high-order decoding information and hence diminish the need for a high-order decoder.
In this paper, we empirically evaluate the combinations of different neural and non-neural encoders with first- and second-order decoders and provide a comprehensive analysis about the effectiveness of these combinations with varied training data sizes.
We find that: first, when there is large training data, a strong neural encoder with first-order decoding is sufficient to achieve high parsing accuracy and only slightly lags behind the combination of neural encoding and second-order decoding; second, with small training data, a non-neural encoder with a second-order decoder outperforms the other combinations in most cases.

assistant: text: Empirically analyzes various encoders, decoders, and their dependencies for graph-based dependency parsing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: On the Effectiveness of Minimal Context Selection for Robust Question Answering

Paper content:
Machine learning models for question-answering (QA), where given a question and a passage, the learner must select some span in the passage as an answer, are known to be brittle.
By inserting a single nuisance sentence into the passage, an adversary can fool the model into selecting the wrong span.
A promising new approach for QA decomposes the task into two stages:
(i) select relevant sentences from the passage; and
(ii) select a span among those sentences.
Intuitively, if the sentence selector excludes the offending sentence, then the downstream span selector will be robust.
While recent work has hinted at the potential robustness of two-stage QA, these methods have never, to our knowledge, been explicitly combined with adversarial training.
This paper offers a thorough empirical investigation of adversarial robustness, demonstrating that although the two-stage approach lags behind single-stage span selection, adversarial training improves its performance significantly, leading to an improvement of over 22 points in F1 score over the adversarially-trained single-stage model.

assistant: text: This paper investigates an existing model and finds that a two-stage trained QA method is not more robust to adversarial attacks compared to other methods.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: RotationOut as a Regularization Method for Neural Network

Paper content:
In this paper, we propose a novel regularization method, RotationOut, for neural networks.
Different from Dropout that handles each neuron/channel independently, RotationOut regards its input layer as an entire vector and introduces regularization by randomly rotating the vector.
RotationOut can also be used in convolutional layers and recurrent layers with a small modification.
We further use a noise analysis method to interpret the difference between RotationOut and Dropout in co-adaptation reduction.
Using this method, we also show how to use RotationOut/Dropout together with Batch Normalization.
Extensive experiments in vision and language tasks are conducted to show the effectiveness of the proposed method.
Codes will be available.

assistant: text: This paper proposes a new regularization method to mitigate the overfitting issue of deep neural networks by rotating features with a random rotation matrix to reduce co-adaptation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A∗ Search and Bound-Sensitive Heuristics for Oversubscription Planning

Paper content:
Oversubscription planning (OSP) is the problem of finding plans that maximize the utility value of their end state while staying within a specified cost bound.
Recently, it has been shown that OSP problems can be reformulated as classical planning problems with multiple cost functions but no utilities.
Here we take advantage of this reformulation to show that OSP problems can be solved optimally using the A* search algorithm, in contrast to previous approaches that have used variations on branch-and-bound search.
This allows many powerful techniques developed for classical planning to be applied to OSP problems.
We also introduce novel bound-sensitive heuristics, which are able to reason about the primary cost of a solution while taking into account secondary cost functions and bounds, to provide superior guidance compared to heuristics that do not take these bounds into account.
We implement two such bound-sensitive variants of existing classical planning heuristics, and show experimentally that the resulting search is significantly more informed than comparable heuristics that do not consider bounds.

assistant: text: The paper proposes modifications to admissible heuristics to make them better informed in a multi-criteria setting where.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: SELF: Learning to Filter Noisy Labels with Self-Ensembling

Paper content:
Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time.
To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training.
Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels.
For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs.
We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch.
While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss.
We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios.
It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.

assistant: text: This paper proposed "self-ensemble label filtering" for learning with noisy labels where the label noise is instance-independent, which yield more accurate identification of inconsistent predictions.
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: We define a concept of layerwise model-parallel deep neural networks, for which layers operate in parallel, and provide a toolbox to design, train, evaluate, and on-line interact with these networks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming

Paper content:
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
We show how this can be implemented natively in probabilistic programming.
By considering the structure of the counterfactual query, one can significantly optimise the inference process.
We also consider design choices to enable further optimisations.
We introduce MultiVerse, a probabilistic programming prototype engine for approximate causal reasoning.
We provide experimental results and compare with Pyro, an existing probabilistic programming framework with some of causal reasoning tools.

assistant: text: Probabilistic Programming that Natively Supports Causal, Counterfactual Inference
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Unifying semi-supervised and robust learning by mixup

Paper content:
Supervised deep learning methods require cleanly labeled large-scale datasets, but collecting such data is difficult and sometimes impossible.
There exist two popular frameworks to alleviate this problem: semi-supervised learning and robust learning to label noise.
Although these frameworks relax the restriction of supervised learning, they are studied independently.
Hence, the training scheme that is suitable when only small cleanly-labeled data are available remains unknown.
In this study, we consider learning from bi-quality data as a generalization of these studies, in which a small portion of data is cleanly labeled, and the rest is corrupt.
Under this framework, we compare recent algorithms for semi-supervised and robust learning.
The results suggest that semi-supervised learning outperforms robust learning with noisy labels.
We also propose a training strategy for mixing mixup techniques to learn from such bi-quality data effectively.

assistant: text: The authors propose a strategy based on mixup for training a model in a formal setting that includes the semi-supervised and the robust learning tasks as special cases.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning

Paper content:
A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.
In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.
We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.
To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.
We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.
Our experiments show that the proposed methods lead to significant improvements on cooperative problems.
We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.

assistant: text: We propose regularization objectives for multi-agent RL algorithms that foster coordination on cooperative tasks.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Swoosh! Rattle! Thump! - Actions that Sound

Paper content:
Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.
In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound.
This is primarily due to lack of data that captures the interplay of action and sound.
In this work, we perform the first large-scale study of the interactions between sound and robotic action.
To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.
By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.
Using this data, we explore the synergies between sound and action, and present three key insights.
First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.
Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object.
Finally, object representations derived from audio embeddings are indicative of implicit physical properties.
We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.

assistant: text: We explore and study the synergies between sound and action.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Tandem Blocks in Deep Convolutional Neural Networks

Paper content:
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks.
The explanations in the literature for the apparent effectiveness of shortcuts are varied and often contradictory.
We hypothesize that shortcuts work primarily because they act as linear counterparts to nonlinear layers.
We test this hypothesis by using several variations on the standard residual block, with different types of linear connections, to build small (100k--1.2M parameter) image classification networks.
Our experiments show that other kinds of linear connections can be even more effective than the identity shortcuts.
Our results also suggest that the best type of linear connection for a given application may depend on both network width and depth.

assistant: text: We generalize residual blocks to tandem blocks, which use arbitrary linear maps instead of shortcuts, and improve performance over ResNets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: BayesOpt Adversarial Attack

Paper content:
Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.
Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries.
Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost.
We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.
We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.

assistant: text: We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Near-Optimal Representation Learning for Hierarchical Reinforcement Learning

Paper content:
We study the problem of representation learning in goal-conditioned hierarchical reinforcement learning.
In such hierarchical structures, a higher-level controller solves tasks by iteratively communicating goals which a lower-level policy is trained to reach.
Accordingly, the choice of representation -- the mapping of observation space to goal space -- is crucial.
To study this problem, we develop a notion of sub-optimality of a representation, defined in terms of expected reward of the optimal hierarchical policy using this representation.
We derive expressions which bound the sub-optimality and show how these expressions can be translated to representation learning objectives which may be optimized in practice.
Results on a number of difficult continuous-control tasks show that our approach to representation learning yields qualitatively better representations as well as quantitatively better hierarchical policies, compared to existing methods.

assistant: text: This paper proposes a way to handle sub-optimality in the context of learning representations which refer to the sub-optimality of hierarchical polity with respect to the task reward.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: MultiGrain: a unified image embedding for classes and instances

Paper content:
We introduce MultiGrain, a neural network architecture that generates compact image embedding vectors that solve multiple tasks of different granularity: class, instance, and copy recognition.
MultiGrain is trained jointly for classification by optimizing the cross-entropy loss and for instance/copy recognition by optimizing a self-supervised ranking loss.
The self-supervised loss only uses data augmentation and thus does not require additional labels.
Remarkably, the unified embeddings are not only much more compact than using several specialized embeddings, but they also have the same or better accuracy.
When fed to a linear classifier, MultiGrain using ResNet-50 achieves 79.4% top-1 accuracy on ImageNet, a +1.8% absolute improvement over the the current state-of-the-art AutoAugment method.
The same embeddings perform on par with state-of-the-art instance retrieval with images of moderate resolution.
An ablation study shows that our approach benefits from the self-supervision, the pooling method and the mini-batches with repeated augmentations of the same image.

assistant: text: Combining classification and image retrieval in a neural network architecture, we obtain an improvement for both tasks.