Questions tagged [papers]

For questions related to artificial intelligence research papers. So, you should use this tag if you want someone to clarify something in a research paper.

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53 views

What does $r : \mathcal{S} \times \mathcal{A} \rightarrow \mathbb{R}$ mean in the article Hindsight Experience Replay, section 2.1?

Taken from section 2.1 in the article: We consider the standard reinforcement learning formalism consisting of an agent interacting with an environment. To simplify the exposition we assume that the ...
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19 views

What is the purpose of the DAMSM loss for the generators in AttnGAN?

I am confused about the training part in AttnGan. If you observe page 3. There are two types of losses for generator network: one involving the Deep Attentional Multimodal Similarity Model (DAMSM) ...
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1answer
35 views

Are mult-adds and FLOPs equivalent?

I am comparing different CNN architectures for edge implementation. Some papers describing architectures refer to mult-adds, like the MobileNet V1 paper, where it is claimed that this net has 569M ...
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1answer
35 views

Do the rows of the design matrix refer to the observations or predictors?

I attempt to understand the formulation of dictionary learning for this paper: Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution Multimodal Task-Driven ...
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1answer
52 views

Can entire neural networks be composed of only activation functions?

Inverse Reinforcement Learning based on GAIL and GAN-Guided Cost Learning(GAN-GCL), uses a discriminator to classify between expert demos and policy generated samples. Adversarial iRL, build upon GAN-...
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20 views

How does the second phase of the evaluation function described in this article work?

I am trying to create an evaluation function for a general game player based on the research from this article An Automatically-Generated Evaluation Function in General Game Playing. I can't ...
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28 views

How to understand this NN architecture?

I was reading a paper Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks and I was stuck understanding the deep neural network architecture that was used. The ...
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19 views

How do non-local neural networks relate to attention and self-attention?

I've been reading non-local neural networks as explained in the original paper. My understanding is that they solve the restrained reception of local filters. I see how they are different from ...
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1answer
77 views

How does the Ornstein-Uhlenbeck process work, and how it is used in DDPG?

In section 3 of the paper Continuous control with deep reinforcement learning, the authors write As detailed in the supplementary materials we used an Ornstein-Uhlenbeck process (Uhlenbeck & ...
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16 views

How to calculate the attention loss in the paper “Tell Me Where to Look: Guided Attention Inference Network”?

I have been reading the research paper Tell Me Where to Look: Guided Attention Inference Network. In this paper, they calculate the attention loss, but I didn't understand how to calculate it. Do we ...
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1answer
49 views

What is the surrogate loss function in imitation learning, and how is it different from the true cost?

I've been reading A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning lately, and I can't understand what they mean by the surrogate loss function. Some relevant ...
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14 views

Limbs for PAFs in OpenPose

What limbs are used in openpose for the PAF? If you look at the skeletal reconstruction one would assume it is $ears - eyes$, $eyes - nose$, $nose-neck$, $neck - hips$, $shoulders - elbows$, $elbows- ...
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21 views

How are the step size and covariance matrix updated in CMA-ES?

I've been following the tutorial The CMA Evolution Strategy: A Tutorial to try and understand the CMA-ES, but I'm having trouble understanding how the step size and the covariance matrix are been ...
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83 views

What is the memory complexity of the memory-efficient attention in Reformer?

When I read the paper, Reformer: The Efficient Transformer, I cannot get the same complexity of the memory-efficient method in Table 1 (p. 5), which summarizes time/memory complexity of scaled dot-...
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74 views

How can a de-noising auto-encoder act as an anomaly detection model?

In some research papers, I have seen that, for training the autoencoders, instead of giving the non-anomalous input images, they add some anomalies to the normal input images, and train the auto-...
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47 views

What is meant by “arranging the final features of CNN in a grid” and how to do it?

In the paper What You Get Is What You See: A Visual Markup Decompiler, the authors have proposed a method to extract the features from the CNN and then arrange those extracted features in a grid to ...
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72 views

Ways to keep up with the latest developments in Machine Learning and AI?

With over 100 papers published in the area of artificial intelligence, machine learning and their subfields every day (source), accounting for ~3% of all publications world wide per year (source) and ...
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1answer
40 views

What is convergence analysis, and why is it needed in reinforcement learning?

While reading a paper about Q-learning in network energy consumption, I came across the section on convergence analysis. Does anyone know what convergence analysis is, and why is convergence analysis ...
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1answer
50 views

What is meant by degrees of freedom of latent variables?

...Designing such a likelihood function is typically challenging; however, we observe that features like spectrogram are effective when latent variables have limited degrees of freedom. This motivates ...
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50 views

What is a heatmap in the CornerNet paper?

I have been working on understanding how CornerNet works, but I couldn't figure out a few parts about the architecture. First, the authors mention that there are 3 distinct parts to be predicted as a ...
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24 views

What is the score used to visualize attention in this paper?

I'm reading this paper Global-Locally Self-Attentive Dialogue State Tracker and follow through the implementation published in GLAD. I was wondering if someone can clarify what variable or score is ...
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1answer
45 views

What does it mean when a model “statistically outperforms” another?

I was reading this paper where they are stating the following: We also use the T-Test to test the significance of GMAN in 1 hour ahead prediction compared to Graph WaveNet. The p-value is less than 0....
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29 views

How are the coefficients of the Region of Interest being selected?

I was reading the following paper: Rl-Ncs: Reinforcement Learning Based Data-Driven Approach For Nonuniform Compressed Sensing, and my question is: how do they decide whether a signal is characterized ...
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4answers
172 views

How can I read any AI paper?

I have studied linear algebra, probability, and calculus twice. But I don't understand how can I reach the level that I can read any AI paper and understand mathematical notation in it. What is your ...
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1answer
42 views

What do the notations $\sim$ and $\Delta (A) $ mean in the paper “Fairness Through Awareness”?

In this paper Fairness Through Awareness, the notation $\mathbb{E}_{x \sim V} \mathbb{E}_{a \sim \mu_x} L(x,a)$ is being used (page 5 top line), where $V$ denotes the set of individuals (so I guess ...
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1answer
52 views

Why should the baseline's prediction be near zero, according to the Integrated Gradients paper?

I am trying to understand Intagrated Gradients, but have difficulty in understanding the authors' claim (in section 3, page 3): For most deep networks, it is possible to choose a baseline such that ...
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0answers
25 views

Deriving hyperparameter updates in Online Interactive Collaborative Filtering

I've been going through "Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms" by Wang et al. and am unable to understand how the update equations for the ...
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0answers
34 views

Why can't neural networks be applied to preference learning problems?

In section 6.1 of the paper Neural Networks in Economics, the authors say this leads to the problem, that no risk can be formulated which shall be minimized by a Neural Network learning algorithm. ...
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34 views

Are the final states not being updated in this $n$-step Q-Learning algorithm?

I am reading this paper and in algorithm 3 they describe an $n$-step Q-Learning algorithm. Below is the pseudo-code. From this pseudo-code, it looks as though the final tuples that they would ...
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1answer
32 views

What is the main contribution of the paper Disentangling by Factorising?

Considering the paper Disentangling by Factorising, in addition to introducing a new model for Disentangled Representation Learning, FactorVAE (see figure), what is the main theoretical contribution ...
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1answer
38 views

What are finite horizon look-ahead policies in reinforcement learning?

I was reading the paper How to Combine Tree-Search Methods in Reinforcement Learning published in AAAI Conference 2019. It starts with the sentence Finite-horizon lookahead policies are abundantly ...
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1answer
83 views

How does publishing in the deep learning world work, with respect to journals and arXiv?

Let's say I implemented a new deep learning model that pushed some SOTA a little bit further, and I wrote a new paper about for publication. How does it work now? I pictured three options: Submit it ...
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0answers
47 views

What is a Hidden Markov Model - Artificial Neural Network (HMM-ANN)?

As far as I know, neural networks have hidden computational units and HMM has hidden states. Hidden Markov Models can be used to generate a language, that is, list elements from a family of strings. ...
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1answer
41 views

Understanding the node information score in the paper “Hierarchical Graph Pooling with Structure Learning”

The paper Hierarchical Graph Pooling with Structure Learning (2019) introduces a distance measure between: a graph's node-representation matrix $\text{H}$, and an approximation of this constructed ...
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3answers
4k views

Why do most deep learning papers not include an implementation?

I'm a novice researcher, and as I started to read papers in the area of deep learning I noticed that the implementation is normally not added and is needed to be searched elsewhere, and my question is ...
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0answers
31 views

What do the authors of this paper mean by the bias term in this picture of a neural network implementation?

I am reading a paper implementing a deep deterministic policy gradient algorithm for portfolio management. My question is about a specific neural network implementation they depict in this picture (...
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0answers
55 views

Why is diversity of reasoning paths important in recommender systems using knowledge graphs?

This is a continuation of the discussion that originates on this StackExchange post, about recommender systems using knowledge graphs(KGs). For those who might not prefer reading the original post, I ...
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1answer
49 views

Which reward function works for recommendation systems using knowledge graphs?

I've been reading this paper on recommendation systems using reinforcement learning (RL) and knowledge graphs (KGs). To give some background, the graph has several (finitely many) entities, of which ...
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0answers
26 views

How can transition models in RL be trained adversarially?

To give a little background, I've been reading the COBRA paper, and I've reached the section that talks about the exploration policy, in particular. We figure that a uniformly random policy won't do ...
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2answers
35 views

What is meant by a multi-dimensional continuous action space?

In the context of Reinforcement Learning, what does it mean to have a multi-dimensional continuous action space? I came across the following in the COBRA Paper - "A method for learning a ...
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0answers
24 views

Understanding the Objective of Neural Expectation Maximization (N-EM)

"The goal of N-EM is to group pixels in the input that belong to the same object (perceptual grouping) and capture this information efficiently in a distributed representation $θ_k$ for each object. ...
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0answers
28 views

Why is this variable in equation 2 of the SQAIR paper a random vector of $n$ ones followed by a zero?

I've been reading the SQAIR paper lately, and the mathematics involved seems a bit complicated. Some background, about the paper: SQAIR stands for Sequential Attend, Infer, Repeat - the paper does ...
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2answers
70 views

How can Siamese Networks be viewed as RNNs?

"Single-object tracking commonly uses Siamese networks, which can be seen as an RNN unrolled over two time-steps." (from the SQAIR paper) I'm wondering how Siamese networks can be viewed as RNNs, as ...
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1answer
59 views

What is the KWIK Framework?

"...for learning transition dynamics...in the KWIK framework." The above is part of a paper's conclusion - and I don't really seem to understand what the KWIK framework is. In the details of the ...
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0answers
47 views

How are the classical MDP and the object-oriented MDP views different?

I've been reading the attached paper - which aims to model entities in the world as objects, including the learning agent itself! To say the least, the goal is to navigate through what seems like a ...
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1answer
28 views

Why do we set offset (0.5) in single shot detector?

In the paper SSD: Single Shot MultiBox Detector, under section 2.2 - (4), why do we add an offset of 0.5 to x, y in generating the anchor boxes across feature maps?
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1answer
50 views

Does the paper “On the difficulty of training Recurrent Neural Networks” (2013) assume, falsely, that spectral radii are $\ge$ square matrix norms?

(link to paper in arxiv) In section 2.1 the authors define $\gamma$ as the maximum possible value of the derivative of the activation function (e.g. 1 for tanh.) Then they have this to say: We ...
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1answer
31 views

What does “In each generation, 25% of offspring resulted from mutation without crossover” mean in the context of NEAT?

I am reading through the NEAT paper. In parameter settings, page 15, there is: In each generation, 25% of offspring resulted from mutation without crossover. What does it mean?
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1answer
36 views

Is the paper “Reducing the Dimensionality of Data with Neural Networks” by Hinton relevant?

Is the paper "Reducing the Dimensionality of Data with Neural Networks" by G. Hinton and R. Salakhutdinov relevant? It seems that the deep learning textbook by Goodfellow, Bengio & Courville (...
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139 views

Understanding the results of “Visualizing and Understanding Convolutional Networks”

I am trying to understand the results of the paper Visualizing and Understanding Convolutional Networks, in particular the following image: What are these 3x3 blocks and their 9 cells representing? ...