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

Title: Data Augmentation Generative Adversarial Networks

Paper content:
Effective training of neural networks requires much data.
In the low-data regime,
parameters are underdetermined, and learnt networks generalise poorly.
Data
Augmentation (Krizhevsky et al., 2012) alleviates this by using existing data
more effectively.
However standard data augmentation produces only limited
plausible alternative data.
Given there is potential to generate a much broader set
of augmentations, we design and train a generative model to do data augmentation.
The model, based on image conditional Generative Adversarial Networks, takes
data from a source domain and learns to take any data item and generalise it
to generate other within-class data items.
As this generative process does not
depend on the classes themselves, it can be applied to novel unseen classes of data.
We show that a Data Augmentation Generative Adversarial Network (DAGAN)
augments standard vanilla classifiers well.
We also show a DAGAN can enhance
few-shot learning systems such as Matching Networks.
We demonstrate these
approaches on Omniglot, on EMNIST having learnt the DAGAN on Omniglot, and
VGG-Face data.
In our experiments we can see over 13% increase in accuracy in
the low-data regime experiments in Omniglot (from 69% to 82%), EMNIST (73.9%
to 76%) and VGG-Face (4.5% to 12%); in Matching Networks for Omniglot we
observe an increase of 0.5% (from 96.9% to 97.4%) and an increase of 1.8% in
EMNIST (from 59.5% to 61.3%).

assistant: text: The authors propose a method to conduct data augmentation where the cross-class transformations are mapped to a low dimensional latent space using conditional GAN
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

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

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

assistant: text: The authors propose a method for learning smoother convolutional kernels, specifically, a regularizer penalizing large changes between consecutive pixels of the kernel with the intuition of penalizing the use of high-frequency input components.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Difference-Seeking Generative Adversarial Network

Paper content:
We propose a novel algorithm, Difference-Seeking Generative Adversarial Network (DSGAN), developed from traditional GAN.
DSGAN considers the scenario that the training samples of target distribution, $p_{t}$, are difficult to collect.
Suppose there are two distributions  $p_{\bar{d}}$ and $p_{d}$ such that the density of the target distribution can be the differences between the densities of $p_{\bar{d}}$ and $p_{d}$.
We show how to learn the target distribution $p_{t}$ only via samples from $p_{d}$ and $p_{\bar{d}}$ (relatively easy to obtain).
DSGAN has the flexibility to produce samples from various target distributions (e.g. the out-of-distribution).
Two key applications, semi-supervised learning and adversarial training, are taken as examples to validate the effectiveness of DSGAN.
We also provide theoretical analyses about the convergence of DSGAN.
In machine learning, how to learn a probability distribution is usually conducted in a unsupervised learning manner.
Generative approaches are developed for learning data distribution from its samples and thereafter produce novel and high-dimensional samples from learned distributions, such as image and speech synthesis BID18 ).
The state-of-the-art approaches is so-called Generative Adversarial Networks (GAN) BID6 ).
GAN produces sharp images based on a game-theoretic framework, but can be tricky and unstable to train due to multiple interacting losses.
Specifically, GAN consists of two functions: generator and discriminator.
Both functions are represented as parameterized neural networks.
The discriminator network is trained to classify whether or not inputs belong to real data or fake data created by the generator.
The generator learns to map a sample from a latent space to some distribution to increase the classification errors of discriminator.
GAN corresponds to a minimax two-player game, which ends if the generator actually learns the real data distribution.
The generator is of main interest because the discriminator will be unable to differentiate between both distributions once the generator has been trained well.
In this paper, we propose DSGAN that can produce samples from the target distribution based on the assumption that the density of target distribution can be the difference between the densities of any two distributions.
DSGAN is useful in the environment when the samples from the target distribution are more difficult to collect than those from the two known distributions.
We demonstrate that DSGAN is really applicable to, for example, semi-supervised learning and adversarial training.
Empirical and theoretical results are provided to validate the effectiveness of DSGAN.
Finally, because DSGAN is developed based on traditional GAN, it is easy to extend any improvements of traditional GAN to DSGAN.
We complete this proof.

assistant: text: This paper considers the problem of learning a GAN to capture a target distribution with only very few training samples from that distribution available.
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: This paper explores the connections between action and sound by building a sound-action-vision dataset with a tilt-bot.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: A Simple Geometric Proof for the Benefit of Depth in ReLU Networks

Paper content:
We present a simple proof for the benefit of depth in multi-layer feedforward network with rectifed activation (``"depth separation").
Specifically we present a sequence of classification problems f_i such that
(a) for any fixed depth rectified network we can find an index m such that problems with index > m require exponential network width to fully represent the function f_m; and
(b) for any problem f_m in the family, we present a concrete neural network with linear depth and bounded width that fully represents it.
While there are several previous work showing similar results, our proof uses substantially simpler tools and techniques, and should be accessible to undergraduate students in computer science and people with similar backgrounds.

assistant: text: ReLU MLP depth seperation proof with gemoteric arguments
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: This paper studies a multiagent dialog task in which the learning agent aims to generate natural language actions that elicit a particular action from the other agent, and shows RL-agents can achieve higher task completion levels than imitation learning baselines.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Self-Imitation Learning via Trajectory-Conditioned Policy for Hard-Exploration Tasks

Paper content:
Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards.
However, it is very difficult to achieve similar success without relying on expert demonstrations.
Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior.
To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks.
We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.
Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima.
In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states.

assistant: text: Self-imitation learning of diverse trajectories with trajectory-conditioned policy
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Universality, Robustness, and Detectability of Adversarial Perturbations under Adversarial Training

Paper content:
Classifiers such as deep neural networks have been shown to be vulnerable against adversarial perturbations on problems with high-dimensional input space.
While adversarial training improves the robustness of classifiers against such adversarial perturbations, it leaves classifiers sensitive to them on a non-negligible fraction of the inputs.
We argue that there are two different kinds of adversarial perturbations: shared perturbations which fool a classifier on many inputs and singular perturbations which only fool the classifier on a small fraction of the data.
We find that adversarial training increases the robustness of classifiers against shared perturbations.
Moreover, it is particularly effective in removing universal perturbations, which can be seen as an extreme form of shared perturbations.
Unfortunately, adversarial training does not consistently increase the robustness against singular perturbations on unseen inputs.
However, we find that adversarial training decreases robustness of the remaining perturbations against image transformations such as changes to contrast and brightness or  Gaussian blurring.
It thus makes successful attacks on the classifier in the physical world less likely.
Finally, we show that even singular perturbations can be easily detected and must thus exhibit generalizable patterns even though the perturbations are specific for certain inputs.

assistant: text: The authors show that adversarial training is effective in protecting against "shared" adversarial perturbation, in particular against universal perturbation, but less effective to protect against singular perturbations.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Fully Quantized Transformer for Improved Translation

Paper content:
State-of-the-art neural machine translation methods employ massive amounts of parameters.
Drastically reducing computational costs of such methods without affecting performance has been up to this point unsolved.
In this work, we propose a quantization strategy tailored to the Transformer architecture.
We evaluate our method on the WMT14 EN-FR and WMT14 EN-DE translation tasks and achieve state-of-the-art quantization results for the Transformer, obtaining no loss in BLEU scores compared to the non-quantized baseline.
We further compress the Transformer by showing that, once the model is trained, a good portion of the nodes in the encoder can be removed without causing any loss in BLEU.

assistant: text: A method for reducing the required memory space by a quantization technique, focused on reducing it for Transformer architecture.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Scoring-Aggregating-Planning: Learning task-agnostic priors from interactions and sparse rewards for zero-shot generalization

Paper content:
Humans can learn task-agnostic priors from interactive experience and utilize the priors for novel tasks without any finetuning.
In this paper, we propose Scoring-Aggregating-Planning (SAP), a framework that can learn task-agnostic semantics and dynamics priors from arbitrary quality interactions as well as the corresponding sparse rewards and then plan on unseen tasks in zero-shot condition.
The framework finds a neural score function for local regional state and action pairs that can be aggregated to approximate the quality of a full trajectory; moreover, a dynamics model that is learned with self-supervision can be incorporated for planning.
Many of previous works that leverage interactive data for policy learning either need massive on-policy environmental interactions or assume access to expert data while we can achieve a similar goal with pure off-policy imperfect data.
Instantiating our framework results in a generalizable policy to unseen tasks.
Experiments demonstrate that the proposed method can outperform baseline methods on a wide range of applications including gridworld, robotics tasks and video games.

assistant: text: A method aiming to learn task-agnostic priors for zero-shot generalization, with the idea to employ a modeling approach on top of the model-based RL framework.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Role of two learning rates in convergence of model-agnostic meta-learning

Paper content:
Model-agnostic meta-learning (MAML) is known as a powerful meta-learning method.
However, MAML is notorious for being hard to train because of the existence of two learning rates.
Therefore, in this paper, we derive the conditions that inner learning rate $\alpha$ and meta-learning rate $\beta$ must satisfy for MAML to converge to minima with some simplifications.
We find that the upper bound of $\beta$ depends on $ \alpha$, in contrast to the case of using the normal gradient descent method.
Moreover, we show that the threshold of $\beta$ increases as $\alpha$ approaches its own upper bound.
This result is verified by experiments on various few-shot tasks and architectures; specifically, we perform sinusoid regression and classification of Omniglot and MiniImagenet datasets with a multilayer perceptron and a convolutional neural network.
Based on this outcome, we present a guideline for determining the learning rates: first, search for the largest possible $\alpha$; next, tune $\beta$ based on the chosen value of $\alpha$.

assistant: text: The authors tackled the optimization instability problem in MAML by investigating the two learning rates.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Robust Joint Representations for Multimodal Sentiment Analysis

Paper content:
Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities.
The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities.
However, existing work learns joint representations using multiple modalities as input and may be sensitive to noisy or missing modalities at test time.
With the recent success of sequence to sequence models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time.
In this paper, we propose a method to learn robust joint representations by translating between modalities.
Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.
We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities.
Once our translation model is trained with paired multimodal data, we only need data from the source modality at test-time for prediction.
This ensures that our model remains robust from perturbations or missing target modalities.
We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube.
Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to perturbations of all other modalities.

assistant: text: We present a model that learns robust joint representations by performing hierarchical cyclic translations between multiple modalities.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Online abstraction with MDP homomorphisms for Deep Learning

Paper content:
Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy.
In this paper, we propose a new algorithm for finding abstract MDPs in environments with continuous state spaces.
It is based on MDP homomorphisms, a structure-preserving mapping between MDPs.
We demonstrate our algorithm's ability to learns abstractions from collected experience and show how to reuse the abstractions to guide exploration in new tasks the agent encounters.
Our novel task transfer method beats a baseline based on a deep Q-network.

assistant: text: A methodology that uses the idea of MDP homomorphisms to transform a complex MDP with a continuous state space to a simpler one.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training

Paper content:
Existing neural networks are vulnerable to "adversarial examples"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks.
The most investigated defense strategy is adversarial training which augments training data with adversarial examples.
However, applying single-step adversaries in adversarial training does not support the robustness of the networks, instead, they will even make the networks to be overfitted.
In contrast to the single-step, multi-step training results in the state-of-the-art performance on MNIST and CIFAR10, yet it needs a massive amount of time.
Therefore, we propose a method, Stochastic Quantized Activation (SQA) that solves overfitting problems in single-step adversarial training and fastly achieves the robustness comparable to the multi-step.
SQA attenuates the adversarial effects by providing random selectivity to activation functions and allows the network to learn robustness with only single-step training.
Throughout the experiment, our method demonstrates the state-of-the-art robustness against one of the strongest white-box attacks as PGD training, but with much less computational cost.
Finally, we visualize the learning process of the network with SQA to handle strong adversaries, which is different from existing methods.

assistant: text: The paper proposes a model to improve adversarial training by introducing random perturbations in the activations of one of the hidden layers
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Part-Based 3D Face Morphable Model with Anthropometric Local Control

Paper content:
We propose an approach to construct realistic 3D facial morphable models (3DMM) that allows an intuitive facial attribute
editing workflow.
Current face modeling methods using 3DMM suffer from the lack of local control.
We thus create a 3DMM by
combining local part-based 3DMM for the eyes, nose, mouth, ears, and facial mask regions.
Our local PCA-based approach
uses a novel method to select the best eigenvectors from the local 3DMM to ensure that the combined 3DMM is expressive
while allowing accurate reconstruction.
The editing controls we provide to the user are intuitive as they are extracted from
anthropometric measurements found in the literature.
Out of a large set of possible anthropometric measurements, we filter the
ones that have meaningful generative power given the face data set.
We bind the measurements to the part-based 3DMM through
mapping matrices derived from our data set of facial scans.
Our part-based 3DMM is compact yet accurate, and compared to
other 3DMM methods, it provides a new trade-off between local and global control.
We tested our approach on a data set of 135
scans used to derive the 3DMM, plus 19 scans that served for validation.
The results show that our part-based 3DMM approach
has excellent generative properties and allows intuitive local control to the user.

assistant: text: This paper describes a method of part-based morphable facial model allowing for localized user control.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hidden incentives for self-induced distributional shift

Paper content:
Decisions made by machine learning systems have increasing influence on the world.
Yet it is common for machine learning algorithms to assume that no such influence exists.
An example is the use of the i.i.d.
assumption in online learning for applications such as content recommendation, where the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users.
Generally speaking, it is possible for an algorithm to change the distribution of its own inputs.
We introduce the term self-induced distributional shift (SIDS) to describe this phenomenon.
A large body of work in reinforcement learning and causal machine learning aims to deal with distributional shift caused by deploying learning systems previously trained offline.
Our goal is similar, but distinct: we point out that changes to the learning algorithm, such as the introduction of meta-learning, can reveal hidden incentives for distributional shift (HIDS), and aim to diagnose and prevent problems associated with hidden incentives.
We design a simple  environment as a "unit test" for HIDS, as well as a content recommendation environment which allows us to disentangle different types of SIDS.
We demonstrate the potential for HIDS to cause unexpected or undesirable behavior in these environments, and propose and test a mitigation strategy.

assistant: text: Performance metrics are incomplete specifications; the ends don't always justify the means.
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.
While existing literature largely focused on the vulnerability of learned models, we demonstrate an intriguing phenomenon that 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 adversarially trained model that is both trained and evaluated on the new distribution.
We show this by constructing semantically- identical variants for MNIST and CIFAR10 respectively, and show that standardly trained models achieve similar 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: AN ATTENTION-BASED DEEP NET FOR LEARNING TO RANK

Paper content:
In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query.
Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism.
This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion.
The embeddings are trained with convolutional neural networks or the word2vec model.
We demonstrate the performance of this model with image retrieval and text querying data sets.

assistant: text: Proposes to use attention to combine multiple input representations for both query and search results in the learning to rank task.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Minimizing Change in Classifier Likelihood to Mitigate Catastrophic Forgetting

Paper content:
Continual learning is a longstanding goal of artificial intelligence, but is often counfounded by catastrophic forgetting that prevents neural networks from learning tasks sequentially.
Previous methods in continual learning have demonstrated how to mitigate catastrophic forgetting, and learn new tasks while retaining performance on the previous tasks.
We analyze catastrophic forgetting from the perspective of change in classifier likelihood and propose a simple L1 minimization criterion which can be adapted to different use cases.
We further investigate two ways to minimize forgetting as quantified by this criterion and propose strategies to achieve finer control over forgetting.
Finally, we evaluate our strategies on 3 datasets of varying difficulty and demonstrate improvements over previously known L2 strategies for mitigating catastrophic forgetting.

assistant: text: This paper introduces a framework for combatting catastrophic forgetting based upon changing the loss term to minimise changes in classifier likelihood, obtained via a Taylor series approximation.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Model-Agnostic Feature Selection with Additional Mutual Information

Paper content:
Answering questions about data can require understanding what parts of an input X influence the response Y. Finding such an understanding can be built by testing relationships between variables through a machine learning model.
For example, conditional randomization tests help determine whether a variable relates to the response given the rest of the variables.
However, randomization tests require users to specify test statistics.
We formalize a class of proper test statistics that are guaranteed to select a feature when it provides information about the response even when the rest of the features are known.
We show that f-divergences provide a broad class of proper test statistics.
In the class of f-divergences, the KL-divergence yields an easy-to-compute proper test statistic that relates to the AMI.
Questions of feature importance can be asked at the level of an individual sample.
We show that estimators from the same AMI test can also be used to find important features in a particular instance.
We provide an example to show that perfect predictive models are insufficient for instance-wise feature selection.
We evaluate our method on several simulation experiments, on a genomic dataset, a clinical dataset for hospital readmission, and on a subset of classes in ImageNet.
Our method outperforms several baselines in various simulated datasets, is able to identify biologically significant genes, can select the most important predictors of a hospital readmission event, and is able to identify distinguishing features in an image-classification task.

assistant: text: We develop a simple regression-based model-agnostic feature selection method to interpret data generating processes with FDR control, and outperform several popular baselines on several simulated, medical, and image datasets.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Emergent Communication through Negotiation

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

assistant: text: The authors describe a variant of the negotation game with the consideration of a secondary communication channel for cheap talk, finding that the secondary channel improves negotation outcomes.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: An Explicitly Relational Neural Network Architecture

Paper content:
With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.
In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity.
We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures.
The workings of a successfully trained model are visualised to shed some light on how the architecture functions.

assistant: text: We present an end-to-end differentiable architecture that learns to map pixels to predicates, and evaluate it on a suite of simple relational reasoning tasks
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Context Dependent Modulation of Activation Function

Paper content:
We propose a modification to traditional Artificial Neural Networks (ANNs), which provides the ANNs with new aptitudes motivated by biological neurons.
Biological neurons work far beyond linearly summing up synaptic inputs and then transforming the integrated information.
A biological neuron change firing modes accordingly to peripheral factors (e.g., neuromodulators) as well as intrinsic ones.
Our modification connects a new type of ANN nodes, which mimic the function of biological neuromodulators and are termed modulators, to enable other traditional ANN nodes to adjust their activation sensitivities in run-time based on their input patterns.
In this manner, we enable the slope of the activation function to be context dependent.
This modification produces statistically significant improvements in comparison with traditional ANN nodes in the context of Convolutional Neural Networks and Long Short-Term Memory networks.

assistant: text: We propose a modification to traditional Artificial Neural Networks motivated by the biology of neurons to enable the shape of the activation function to be context dependent.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Dynamic Channel Pruning: Feature Boosting and Suppression

Paper content:
Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.
In this paper, we reduce this cost by exploiting the fact that the importance of features computed by convolutional layers is highly input-dependent, and propose feature boosting and suppression (FBS), a new method to predictively amplify salient convolutional channels and skip unimportant ones at run-time.
FBS introduces small auxiliary connections to existing convolutional layers.
In contrast to channel pruning methods which permanently remove channels, it preserves the full network structures and accelerates convolution by dynamically skipping unimportant input and output channels.
FBS-augmented networks are trained with conventional stochastic gradient descent, making it readily available for many state-of-the-art CNNs.
We compare FBS to a range of existing channel pruning and dynamic execution schemes and demonstrate large improvements on ImageNet classification.
Experiments show that FBS can respectively provide 5× and 2× savings in compute on VGG-16 and ResNet-18, both with less than 0.6% top-5 accuracy loss.

assistant: text: Proposal for a channel pruning method for dynamically selecting channels during testing.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Hidden incentives for self-induced distributional shift

Paper content:
Decisions made by machine learning systems have increasing influence on the world.
Yet it is common for machine learning algorithms to assume that no such influence exists.
An example is the use of the i.i.d.
assumption in online learning for applications such as content recommendation, where the (choice of) content displayed can change users' perceptions and preferences, or even drive them away, causing a shift in the distribution of users.
Generally speaking, it is possible for an algorithm to change the distribution of its own inputs.
We introduce the term self-induced distributional shift (SIDS) to describe this phenomenon.
A large body of work in reinforcement learning and causal machine learning aims to deal with distributional shift caused by deploying learning systems previously trained offline.
Our goal is similar, but distinct: we point out that changes to the learning algorithm, such as the introduction of meta-learning, can reveal hidden incentives for distributional shift (HIDS), and aim to diagnose and prevent problems associated with hidden incentives.
We design a simple  environment as a "unit test" for HIDS, as well as a content recommendation environment which allows us to disentangle different types of SIDS.
We demonstrate the potential for HIDS to cause unexpected or undesirable behavior in these environments, and propose and test a mitigation strategy.

assistant: text: The paper generalizes the inherent incentive for the learner to win by making the task easier in meta-learning to a larger class of problems.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Rethinking Generalized Matrix Factorization for Recommendation: The Importance of Multi-hot Encoding

Paper content:
Learning good representations of users and items is crucially important to recommendation with implicit feedback.
Matrix factorization is the basic idea to derive the representations of users and items by decomposing the given interaction matrix.
However, existing matrix factorization based approaches share the limitation in that the interaction between user embedding and item embedding is only weakly enforced by fitting the given individual rating value, which may lose potentially useful information.
In this paper, we propose a novel Augmented Generalized Matrix Factorization (AGMF) approach that is able to incorporate the historical interaction information of users and items for learning effective representations of users and items.
Despite the simplicity of our proposed approach, extensive experiments on four public implicit feedback datasets demonstrate that our approach outperforms state-of-the-art counterparts.
Furthermore, the ablation study demonstrates that by using multi-hot encoding to enrich user embedding and item embedding for Generalized Matrix Factorization, better performance, faster convergence, and lower training loss can be achieved.

assistant: text: The work presents a matrix factorization framework for enforcing the effect of historical data when learning user preferences in collaborative filtering settings.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning Good Policies By Learning Good Perceptual Models

Paper content:
Reinforcement learning (RL) has led to increasingly complex looking behavior in recent years.
However, such complexity can be misleading and hides over-fitting.
We find that visual representations may be a useful metric of complexity, and both correlates well objective optimization and causally effects reward optimization.
We then propose curious representation learning (CRL) which allows us to use better visual representation learning algorithms to correspondingly increase visual representation in policy through an intrinsic objective on both simulated environments and transfer to real images.
Finally, we show better visual representations induced by CRL allows us to obtain better performance on Atari without any reward than other curiosity objectives.

assistant: text: We present a formulation of curiosity as a visual representation learning problem and show that it allows good visual representations in agents.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Empowering Graph Representation Learning with Paired Training and Graph Co-Attention

Paper content:
Through many recent advances in graph representation learning, performance achieved on tasks involving graph-structured data has substantially increased in recent years---mostly on tasks involving node-level predictions.
The setup of prediction tasks over entire graphs (such as property prediction for a molecule, or side-effect prediction for a drug), however, proves to be more challenging, as the algorithm must combine evidence about several structurally relevant patches of the graph into a single prediction.
Most prior work attempts to predict these graph-level properties while considering only one graph at a time---not allowing the learner to directly leverage structural similarities and motifs across graphs.
Here we propose a setup in which a graph neural network receives pairs of graphs at once, and extend it with a co-attentional layer that allows node representations to easily exchange structural information across them.
We first show that such a setup provides natural benefits on a pairwise graph classification task (drug-drug interaction prediction), and then expand to a more generic graph regression setup: enhancing predictions over QM9, a standard molecular prediction benchmark.
Our setup is flexible, powerful and makes no assumptions about the underlying dataset properties, beyond anticipating the existence of multiple training graphs.

assistant: text: This paper injects a multi-head co-attention mechanism in GCN that allows one drug to attend to another drug during drug side effect prediction.
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Ensemble methods and LSTM outperformed other eight machine learning classifiers in an EEG-based BCI experiment

Paper content:
We review eight machine learning classification algorithms to analyze Electroencephalographic (EEG) signals in order to distinguish EEG patterns associated with five basic educational tasks.
There is a large variety of classifiers being used in this EEG-based Brain-Computer Interface (BCI) field.
While previous EEG experiments used several classifiers in the same experiments or reviewed different algorithms on datasets from different experiments, our approach focuses on review eight classifier categories on the same dataset, including linear classifiers, non-linear Bayesian classifiers, nearest neighbour classifiers, ensemble methods, adaptive classifiers, tensor classifiers, transfer learning and deep learning.
Besides, we intend to find an approach which can run smoothly on the current mainstream personal computers and smartphones.
The empirical evaluation demonstrated that Random Forest and LSTM (Long Short-Term Memory) outperform other approaches.
We used a data set which users were conducting five frequently-conduct learning-related tasks, including reading, writing, and typing.
Results showed that these best two algorithms could correctly classify different users with an accuracy increase of  5% to 9%, use each task independently.
Within each subject, the tasks could be recognized with an accuracy increase of  4% to 7%, compared with other approaches.
This work suggests that Random Forest could be a recommended approach (fast and accurate) for current mainstream hardware, while LSTM has the potential to be the first-choice approach when the mainstream computers and smartphones can process more data in a shorter time.

assistant: text: Two Algorithms outperformed eight others on a EEG-based BCI experiment
user: text: Write a concise one-sentence TLDR summary of the scientific paper content below.

Title: Learning from Between-class Examples for Deep Sound Recognition

Paper content:
Deep learning methods have achieved high performance in sound recognition tasks.
Deciding how to feed the training data is important for further performance improvement.
We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning).
Our strategy is to learn a discriminative feature space by recognizing the between-class sounds as between-class sounds.
We generate between-class sounds by mixing two sounds belonging to different classes with a random ratio.
We then input the mixed sound to the model and train the model to output the mixing ratio.
The advantages of BC learning are not limited only to the increase in variation of the training data; BC learning leads to an enlargement of Fisher’s criterion in the feature space and a regularization of the positional relationship among the feature distributions of the classes.
The experimental results show that BC learning improves the performance on various sound recognition networks, datasets, and data augmentation schemes, in which BC learning proves to be always beneficial.
Furthermore, we construct a new deep sound recognition network (EnvNet-v2) and train it with BC learning.
As a result, we achieved a performance surpasses the human level.

assistant: text: We propose an novel learning method for deep sound recognition named BC learning.