Questions tagged [reference-request]
Use when requesting examples of research or research papers, books, articles, blog posts or courses. For example, "Is there any published research about X?" or "What are good examples of Y in research?".
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What are the techniques used to initialize weights for neural networks?
When creating a neural network to predict the impact of risks on the project cost, what techniques are used to initialize the weights provided to the hidden layers and the output layer?
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What is the current state of AI regarding features of Moravec's paradox, such as sensorimotor & perception skills?
Moravec's paradox is the observation in artificial intelligence and robotics that, contrary to traditional assumptions, reasoning requires very little computation, but sensorimotor and perception ...
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Book suggestion about deploying real deep learning models in real world
Can you suggest books about deploying machine learning algorithms on robots, especially on real time stream? I don't know how to deal with latency and other challenges that real time inference/stream ...
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How can Knowledge Graphs be Integrated with Language Models for Semantic Search?
I am exploring the incorporate knowledge graphs (KGs) with language models.
I understand that KGs can provide structured understanding of entities and their relationships which can be crucial for ...
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The “of-a-has” system: Is “has” a logical operator?
Given that Mary is the wife OF John, it follows that Mary is A wife and that John HAS a wife. The same “of-a-has” pattern appears in most of-phrases, files, sets, networks and formalized natural ...
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Is there any well-established work that allows robots to communicate their decision-making using natural language?
I am searching for a well-established work that allows robots to communicate their decision-making using natural language.
For example, a robot's explanation could be "I did [task1] because [...
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Algorithms for community discovery in multigraphs
In order to group unstructured or sem-structured texts for a timeline construction approach, I consider several types of correlations among such texts. These different correlations induce a weighted ...
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Modern graduate-level machine learning books with focus on generative models
I'm looking for a modern machine learning book with graduate-level treatment of more recent topics such as diffusion and generative models, transformers etc.
I have a hard copy of Deep Learning by ...
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Does anyone know about a reference where someone has aggregated the cost optimization strategies for deploying LLMs?
I am looking for a source where someone has mentioned the most commonly used strategies and optimisations to deploy LLMs on consumer hardware. I have read about layer offloading, quantisation methods ...
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Filter distribution of Latent variable models
In this paper https://arxiv.org/pdf/1907.00953.pdf, about stochastic latent variable models, the paper says "We use the
reparameterization trick to sample from the filtering distribution". I ...
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Let's Verify Step by Step: Old wine in new bottles?
In their paper "Let's Verify Step by Step" OpenAI proudly presents a new way of reward learning which shall foster LLMs' capabilities of mathematical and logical reasoning:
We've trained a ...
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Is there any reference about backpropagation of the Transformer's multi-head layer?
Is there any reference about backpropagation of the Transformer's multi-head layer or multi-head attention (MHA)? I have searched various journals but have not found one yet.
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Is there a resource that offers a detailed overview of the gradient flow?
Understanding the concept of "Gradient Flow" can be quite difficult as there is a lack of widely recognized and clearly defined resources that provide a comprehensive explanation. Although ...
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Is there a recommended resource that can provide a detailed overview of the gradient norm?
When it comes to the concept of "Gradient Norm," it can be challenging to find a widely recognized and clearly defined resource that offers a comprehensive explanation. While many search ...
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Where can I find the solutions to the problems in the book "An Introduction to Computational Learning Theory"?
I have been going through "An Introduction to Computational Learning Theory" (Kearns-Vazirani). I don't know if my solutions to the problems are correct and have no other way of checking my ...
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Model-based RL algorithms for continuous state space and finite action space
At the beginning, if I have a complete model $p(s' \mid s, a)$ (an assumed true model that describes the environment well enough) and the reward function $r(s,a,s')$. How can I exploit the model and ...
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References for synthetic images generation from small datasets (~10-50 images)
I'm looking a references (papers / works) for synthetic image generation from small datasets. By small dataset, I mean 10-50 images.
I assume, that the best approaches should be based GAN (cGAN ?) or ...
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Are there public examples of AI models that predicted short-term price well?
This question is inspired by Is there any AI model that predicts short-term stock price well?.
As answered, game theoretically either nobody would reveal such a model or it would have been alread ...
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Today's practicality of Neuro-Fuzzy Systems
I am questioning today's practicality of Neuro-Fuzzy Systems (NFS), which build upon Fuzzy logic, linguistic rules, ANFIS and "standard" reinforcement learning (see this paper for a nice ...
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Reference request: AI panel discussion 2012
I have seen (probably on youtube) the recording of a panel discussion at an AI conference around 2012 (year could be wrong, but close) where the audience was polled for the question of when will AI ...
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References for the theory of pretraining and unsupervised learning to improve subsequent supervised learning
I am not sure if the title of this post uses the correct terminology, so suggestions are welcome.
I have been following a lot of the ideas of using Pre-training methods on neural networks, to improve ...
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Resources for NLP [closed]
I am an undergraduate student in mathematics. I have a fair bit of experience with deep learning in computer vision research and am willing to dabble into NLP. I hope that things won't be very ...
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Machine learning applied to space-time diagrams of cellular automata
I wonder if machine learning has ever been applied to space-time diagrams of cellular automata. What comprises a training set seems clear: a number of space-time diagrams of one or several (elementary)...
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Is there some neural network that implements Least Squares?
I would like to build supervised NN that gets a matrix $A$ and vector $b$ as inputs and returns $x$ as a close result of the Least Squares algorithm for $Ax=b$.
I looked for so works in the field and ...
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Is it possible to train an AI to organize frames in chronological order?
I would like to know if there is a neural network or some other kind of AI that would be able to reconstitute randomly shuffled frames into a video or slideshow that makes chronological sense? The ...
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Tokenization for treelike structures
I'm pretraining a BERT (bigbird) model to use with SMILES encoding of chemicals. This kind of data is a treelike structure in the form of a string with a single bracket type. Usually this tree isn't ...
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Could one still learn a good policy by doing a backprop every fixed number of steps within an episode?
Waiting an entire episode before doing a backprop can build up a very large computational graph, which is a burden on memory. Could one still learn a good policy by doing a backprop every fixed number ...
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What is the depth reached by chess-AI agents on a regular computer?
I'm looking for some reference for the number of lookahead steps typically used by chess agents (Stockfish / Leela Chess Zero / others?)
From a quick search, I found that the answer depends on:
...
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How can I encode angle data to train neural networks?
I am training a neural network where the target data is a vector of angles in radians (between $0$ and $2\pi$).
I am looking for study material on how to encode this data.
Can you supply me with a ...
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Machine Learning Methods commonly used when data are scarse
It is well-known that deep neural networks require lots of data to perform reliably and well. A commonly-cited statistic is that you need at least 10,000 examples per class for a classification ...
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What are some non-RL-based approaches to solving a typical bin assignment problem?
What are some non-RL-based approaches to solving a typical bin assignment problem, i.e., given a set of items (can be multidimensional), find the bin/knapsack/target which best packs (with minimum ...
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How to integrate program synthesis into program maintenance and evolution (if it is possible at all) - possible reference request?
Program synthesis is one of the most active research fields today, e.g. works by Microsoft Research https://arxiv.org/abs/2208.05950 and SalesForce Research https://arxiv.org/abs/2203.13474. I ...
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Are there free and open-source audio versions of Generative AI programs like Stable Diffusion?
Generative AI is being used to create amazing art; first through paid services like Midjourney and now also with free, open source alternatives like Stable Diffusion. Now you can even generate art in ...
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machine learning for a budgeting application
I am interested in finding references and previous applications where prior year budgets are analyzed to provide guidance for a current year budget. Specifically, each year some two thousand items ...
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References on Theoretical Bandit Problem
I am going to start learning the bandit problem and algorithm, especially how to bound the regret. I found the book ``Bandit Algorithms'' but it is not easy to follow. It is based on advanced ...
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How can we approximate infinite horizon MDP with finite horizon MDP in the context of reinforcement learning?
For a given value of "discount factor" (and reward values' range) in fixed finite horizon markov decision process (MDP), upto how many episodes we have to extend this MDP so that we can ...
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Papers on Prompt Engineering
I am into AI in general and NLP in particular. Besides, I have a background in philosophy, and the new LLMs like GPT-3 seem to have exciting capabilities. I want to study prompt engineering (for ...
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Can I minimize a mysterious function by running a gradient descent on her neural net approximations?
So I have this function let call her $F:[0,1]^n \rightarrow \mathbb{R}$ and say $10 \le n \le 100$. I want to find some $x_0 \in [0,1]^n$ such that $F(x_0)$ is as small as possible. I don't think ...
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Is there any work done on topic agnostic binary topic classification?
In the recent preprint paper Tree-based Focused Web Crawling with
Reinforcement Learning a new model is introduced to classify web pages called KwBiLSTM.
The input to this model is a featurized ...
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Deep Learning with Best-so-far instead of Where-you-are
It is my understanding that when training a Deep NN in Tensorflow/PyTorch/... we only keep the current state of the network in memory, except perhaps when we manually decide to save the current ...
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Is there a literature on the time complexity of Neural Networks?
There exist various blog posts describing the time complexity of Fully Connected Neural Networks (1, 2, 3, 4); Convolutional Neural Networks (CNN) (5) and of Long Short-Term Memory (LSTM) networks (6)....
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Is there any proper literature on the types of features that different layers of a deep neural network learn?
Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:
1. Shallow layers tend to recognise more low-level features such as edges and curves.
2. ...
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Is there a standardized method to train a reinforcement learning NN by demonstration?
I'm less familiar with reinforcement learning compared to other neural network learning approaches, so I'm unaware of anything exactly like what I want for an approach. I'm wondering if there are any ...
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Does LSTM provide any unique value or advantages compared to other algorithms, including "vanilla" RNN?
I have heard a lot of hype around LSTM for all kinds of time-series based applications including NLP. Despite this, I haven't seen many (if any) applications of LSTM where LSTM performs uniquely well ...
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Today's Practicality of Bayesian Neural Networks
Just having heard lately about BNNs (wow, ANNs and CNNs are clear; now there's a B? What's that? Ahh, Bayesian ;-)) and quickly getting their main idea and focus, that is, weights not being pure ...
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Current state of the art and datasets for combining NLP and CV?
I was considering a scenario where natural language processing (NLP) and computer vision (CV) are combined, for example in extended reality systems that get as input both natural language and non-...
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Does pairing children with their parents cause any harm (in a genetic program)?
If you pair parents with their children (with a cross-over) does this prevent making individuals which are more fit or does this cause other side effects which are harmful to the genetic process?
I ...
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Is there any variant of perceptron convergence algorithm that ensures uniqueness?
The perceptron convergence algorithm given below ensures the convergence of weights of the perceptron provided enough data points and iterations.
Although it ensures convergence by finally getting a ...
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Why does triplet loss allow to learn a ranking whereas contrastive loss only allows to learn similarity?
I am looking at this lecture, which states (link to exact time):
What the triplet loss allows us in contrast to the contrastive loss is
that we can learn a ranking. So it's not only about similarity, ...
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Are there any guidelines on how to map the state space to integers in the case tabular RL algorithms?
Let's say that you want to solve a problem with a tabular reinforcement learning algorithm, for example, Q-learning. You can represent the value function $Q(s, a)$ as a $|\mathcal{S}|\times |\mathcal{...
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