Questions tagged [deep-learning]

a new area of Machine Learning research concerned with the technologies used for learning hierarchical representations of data, mainly done with deep neural networks (i.e. networks with two or more hidden layers), but also with some sort of Probabilistic Graphical Models.

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can decoder only large language model be fine tuned to perform well at semantic similarity search?

BERT based models are Encoder only which are well suited for text classification, and Semantic Text similarity search (If fine-tuned via sBERT). I want to know whether decoder only models like Llama2, ...
haneulkim's user avatar
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How to find LLM that is best at STS task?

I'm trying to find large language models that maps an embedding vector in proximity if they are semantically similar, in Korean. I tried looking at bunch of leaderboard such as MTEB_ko-ko STS, AI Hub ...
haneulkim's user avatar
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How do transformer-based architectures generate contextual embeddings?

How do transformer-based architectures, such as Roberta, etc., generate contextual embeddings? The issue is, I haven't found any articles that explain this process.
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Activity Classification through LOG file

I have a big dataset containing logs/steps that the user performed on my webpage (for example: Clicking on a "Homepage" button, typing some text in the field, etc.) These steps are labelled ...
Aayushmaan Garg's user avatar
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Fine tuning or just feature extraction or both using Roberta?

I'm reading a program that use the pre-trained Roberta model (roberta-base). The code first extracts word embeddings from each caption in the batch, using the last hidden state of the Roberta model. ...
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Add a custom loss in a public github repository on distillation

I’m trying to implement a custom loss in a public repository regarding knowledge distillation. The link to the repository is the following: " https://github.com/DefangChen/SimKD " The main ...
PiEmmeC's user avatar
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Is RTX 2050 compatible with PyTorch? Is it even CUDA-capable?

The NVIDIA site does not list GTX 2050 as CUDA enabled, and does not list its compute capability. However, if you google "Is RTX 2050 cuda enabled", the first result you get is some NVIDIA ...
Daigaku no Baku's user avatar
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Weighting training instances by time in machine learning models

I am training a neural network based on data whose relevance I think diminishes based on how far each instance is in the past. I've had a look and one way to do this it seems is to 'weight' training ...
joe_credit's user avatar
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Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:3! in DPOTrainer with ec2 G5 12X Large

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Sandun Tharaka's user avatar
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Enhancing Soil Moisture Predictions Using Multimodal Data Integration in Agriculture

I am exploring an interdisciplinary research area involving multimodal data, focusing on agriculture. My study incorporates both visual and tabular data: crop and soil images from three distinct ...
Md Rakib's user avatar
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Method for Combining Text Embeddings and Numeric Features in Deep Learning

I'm working on a deep learning model in TensorFlow to predict if two records within a database refer to the same person. I'm trying to use text features (in the form of embeddings) and numeric ...
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Training Models Directly with Transformer Attention Weights: A Viable Strategy?

I'm currently using pre-trained transformers to extract embeddings for sequence analysis, which are then used in downstream tasks. My process involves using the extracted embeddings as features for ...
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How to solve imbalanced dataset oversampling problem in multi labels-classes instance segmentation task?

I want to use models YOLOv7-seg for instance segmentation of tree species in images. There are 26 species of trees, and each image may contain multiple species. There is a distinction between dominant ...
yuga555's user avatar
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Semantic segmentation sometimes give bad result

I'm training Unet+MobileNetV3 for semantic segmentation objects on real photos using custom dataset and get strange results. I have already accumulated pretty big dataset and constantly improve it by ...
Vladislav D's user avatar
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89 views

What do special tokens used for in Roberta?

When I use this code: ...
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IoU metric for multi class image segmentation task

My input shape is of (168,18). I create batches of size 256 and create my dataset using timeseries_from_Array_dataset. I am visualizing this 2D snapshot of a multivariate timeseries (batch size- 256, ...
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Why was the learning rate decreased for Roberta compared to LSTM?

I'm reading the codebase of a project that uses Bidirectional-LSTM. The learning rate for it is 0.02. Later, someone improved the project by replacing LSTM with Roberta and decreased the learning rate ...
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How to find adjacent neighbours using Python?

Full problem description at stackoverflow I need to find the adjacent neighbours (not necessarily nearest neighbours) to a given point in a multidimensional space. As shown in the screenshot below, I ...
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Why is there a difference in Training Accuracy Output, when the training dataset is the same but the validation dataset is different?

I am looking at the output of a multi-class image segmentation deep learning model. I used U-Net to implement this. I am confused about why the training accuracies are different for a different ...
user10529827's user avatar
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What do these terms mean in the context of Roberta?

When I read articles about Roberta, I often read the terms "transfer learning" and "fine-tuning". Additionally, they also mention "feature extraction". What are the ...
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Why do the Llama 2 weights have eight different files?

I downloaded the weights for Llama 2 (70B-chat). This process created a folder titled "llama-2-70b-chat," which contained 8 files titled consolidated.00.pth, consolidated.01.pth, and so on ...
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What are the differences between Embedding Layer and Roberta Embedding?

I'm reading an article about the Embedding Layer: The Embedding Layer learns word embeddings from raw text. It is initialized with small random numbers and can be learned simultaneously with a neural ...
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Extraction of name from phonetic transcription

I have a use case where I want to extract the name from the phonetic transcription. For example if the phonetic transcription is - “m a j n e j m ɪ z s ʌ m i ɹ z o w ʃ i”, the output should be the ...
Sameer Joshi's user avatar
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What are the differences between contextual embeddings of Bidirectional-LSTM and Transformer?

A Transformer, like Roberta, can generate contextual embeddings using the encoder part, similar to a Bidirectional-LSTM that concatenates hidden states. What are the differences between them ? Are ...
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how to get similar traning data for a test sample?

I want to get the most similar samples for test sample on which the model choose it's - output. SHAP isn't useful because it show the contribution of each feature. I want to get the most similar ...
user3668129's user avatar
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The end-to-end Training Process for Knowledge Distillation

I'm a bit confused on the complete training process for Knowledge Distillation. I was reading the Geoffrey Hinton "Distilling the Knowledge in a Neural Network" 2015 paper and some random ...
Chuu's user avatar
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How to optimize my CNN classification architecture

I have this CNN based model architecture that takes an RGB image. Now I'm trying to change it for a color classification case on an object (10 color classes: white, black, yellow, etc). This current ...
Mary's user avatar
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Custom loss function for collinearity of 3 embeddings

I am trying to implement a loss function that takes as input 3 embeddings and output a value that is proportional to the collinearity of the embeddings. This is to shape the latent space of a ...
vlc's user avatar
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How can I use Time-GPT for pretraining my model

I am mentioning Time-GPT here as a placeholder example. It can be any pretrained model. Suppose I have a dataset that requires some time series prediction. How can I leverage a well-trained model and ...
Mohammad Mosiur's user avatar
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Questions about hidden states of bidirectional LSTMs

I read this in an article about bidirectional LSTM: In bidirectional LSTM, each word corresponds to two hidden states, one for each direction. Thus, we concatenate these two hidden states to ...
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Why Latent Space in Stable Diffusion has shape 64x64x3?

I am wondering why the dimensionality of Latent Space in Stable Diffusion is 64x64x3. Since ...
Renat Abdrakhmanov's user avatar
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How to detect abnormal fetal head size with image classification?

I'm writing Python code to predict fetal head circumference 10mm range using classification. The model will train to classify a fetal head image into a range (e.g., 50–60 mm) representing its ...
NiStack's user avatar
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Train Reward Model using Llama2:

this is my code that use to train reward model: ...
Sandun Tharaka's user avatar
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1 answer
116 views

Is vision transformer (ViT) always better than CNN?

The paper - AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE proposed vision transformer and outperformed CNN-based models in many cases. When it comes to sequential data, we ...
Chuck Liu's user avatar
2 votes
1 answer
591 views

What are the differences between BPE and byte-level BPE?

In Roberta, I'm not sure if the model use BPE or byte-level BPE tokenization, are these techniques different or the same ? Can someone explain ? Thanks
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How do I shape my output data for a time series classification problem using LSTM

I am wanting to use an LSTM for anomaly detection on a multivariate time series data. Let's say there are n rows each corresponding to a timestamp incrementing by an hour and d input features and d ...
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Bulding Deep Learning model for multiclassification case

I am soo confused i read a lot of information in forumas and still cna't get what is wrong. my data is around 500.000 rows and 32 columns. my target variables consists of 3 classes (0, 1, 2). Hyperopt ...
Shamkhal Mammadov's user avatar
2 votes
1 answer
86 views

What is the difference between hidden states in RNN and Transformers model?

I'm very terrible at NLP and I have searched for these questions but didn't find any answer, my question is, in RNNs, there are hidden states to remember information for processing the next state, and ...
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Why not Back propagate through time in LSTM , similar to RNN

I'm trying to implement RNN and LSTM , many-to-many architecture. I reasoned myself why BPTT is necessary in RNNs and it makes sense. But what doesn't make sense to me is, most of resources I went ...
Amith Adiraju's user avatar
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Anomaly Detection in Log Data using LSTM

Problem Overview: I am currently working on a project involving anomaly detection in log data. The anomalies are defined by deviations from historical patterns. The log data has a simple structure: [...
Raj's user avatar
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24 views

Tile color, shape, and type detection altogether

I'm trying to apply some detections/classification on the set of tiles we have. Specifically, I need to detect color (15 classes), pattern (25 classes- on the surface of tiles, there can be certain ...
Mary's user avatar
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Clarifying the arguments of "Understanding the difficulty of training deep feedforward neural networks"

I made the decision to try to push through the paper "Understanding the difficulty of training deep feedforward neural networks". (The paper is given as a reference in "Hands-On Machine ...
Chris's user avatar
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Is it fair to say that Hausdorff Distance (HD) focuses on low level details while dice score (DSC) high level

I wonder if its make sense to say that Hausdorff Distance (HD) measures low-level details while dice score (DSC) focuses on high levels. If you could cite a paper, I would appreciate it.
user836026's user avatar
1 vote
2 answers
130 views

Seeking Guidance on Constrained Input Modeling for Soil Moisture Correction Using Rainfall Observations

I find myself immersed in the intricacies of working with 2D modeled fields (images) representing soil moisture in regions where direct observations are unfortunately absent. However, there is a ...
Seyed Omid Nabavi's user avatar
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9 views

How to calibrate IMU for large scale deployments possibly using deep neural network

We were testing our visual SLAM algorithm on robots. We were getting poor performance. Then we calculated wite noise and random walk parameters (using kalibr) for the IMU and used it in our algorithm ...
Mahesha999's user avatar
1 vote
1 answer
41 views

text extraction from bank statements from pdf format

I have bank statement memos containing transaction tables I need to extract. I only need to extract the transactions list. I have tried to use the Amazon text extractor, LayoutLM but since every bank ...
Abduhoshim's user avatar
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23 views

why does my multi-modal model can not learn anything?

I have a multi-modal model. I want to train it using the Pytorch Framework. I have a balanced dataset. I have approximately 150 samples for each client. (I had preprocessed my text data.) when I train ...
arcane_data's user avatar
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1 answer
52 views

How to automate the restarting of training of deep learning model in TensorFlow

I am trying to automate the (recursively) restart of a finished deep-learning training session in TensorFlow. Currently, to restart I am manually restarting my kernel and re-running the training code. ...
user10529827's user avatar
1 vote
1 answer
21 views

Building a CNN (with Keras for pixelwise classification)

I have a set of 120x120 input images with 3 channels. I want to build a basic CNN to predict the value of each pixel. I have 2 doubts. One is regarding the last layer - should be a Dense layer, or a ...
Filippo Nunes's user avatar
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1 answer
35 views

batch Normalization and Layer Normalization difference

In Batch Normalization, mean and standard deviation are calculated feature wise and normalization step is done instance wise and in Layer Normalization mean and standard deviation are calculated ...
April's user avatar
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