Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1274": ["1275", "1263"], "1177": ["1178", "980", "1276"], "853": ["854", "1022"], "1093": ["847", "886", "1351"], "965": []}, "sentence_to_aspect_id": {"I am interested in investigating linguistics from a statistical and mathematical standpoint.": ["1274"], "My focus is on modeling linguistic patterns and phenomena as components of a complex system.": ["1274"], "I aim to compute statistical and information-theoretic features from extensive streams of natural language texts.": ["1177"], "Ideally, these features would provide insights into modeling the long-term syntactical and semantic dependencies between various linguistic elements within a lengthy text stream.": ["1177"], "Moreover, I am keen on modeling human language as a complex communication system regulated by the principles of information theory.": ["853"], "The ultimate objective is to reveal hidden patterns and enhance or consolidate existing patterns using the language of information theory and statistics.": ["1093"], "Therefore, I need a thorough review of recent advancements at the intersection of linguistics and complex systems.": ["965"]}, "aspect_id_to_sentence": {"1274": ["I am interested in investigating linguistics from a statistical and mathematical standpoint.", "My focus is on modeling linguistic patterns and phenomena as components of a complex system."], "1177": ["I aim to compute statistical and information-theoretic features from extensive streams of natural language texts.", "Ideally, these features would provide insights into modeling the long-term syntactical and semantic dependencies between various linguistic elements within a lengthy text stream."], "853": ["Moreover, I am keen on modeling human language as a complex communication system regulated by the principles of information theory."], "1093": ["The ultimate objective is to reveal hidden patterns and enhance or consolidate existing patterns using the language of information theory and statistics."], "965": ["Therefore, I need a thorough review of recent advancements at the intersection of linguistics and complex systems."]}, "idea_from": [320574], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am interested in investigating linguistics from a statistical and mathematical standpoint. My focus is on modeling linguistic patterns and phenomena as components of a complex system. I aim to compute statistical and information-theoretic features from extensive streams of natural language texts. Ideally, these features would provide insights into modeling the long-term syntactical and semantic dependencies between various linguistic elements within a lengthy text stream. Moreover, I am keen on modeling human language as a complex communication system regulated by the principles of information theory. The ultimate objective is to reveal hidden patterns and enhance or consolidate existing patterns using the language of information theory and statistics. Therefore, I need a thorough review of recent advancements at the intersection of linguistics and complex systems."}


With such a model, I can help more people to learn Frisian language. I am trying to train a model that can translate Frisian language documents. To do this, I need a parser that uses a corpus of words in sentences with annotation and uses a tagger to assign tags to each word. I hope this parser will be designed specifically for the West Frisian language so a Dutch tagger may work well. Then, I am looking for some state-of-the-art translation program that can identify the morphological and syntactic structures of the text. I need some suggestions for my model to combine the parser and the translation program’s function and generate annotations based on the morphological and syntactic structure of the translated text.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1489": ["1488", "1490"], "1492": ["1475", "1474", "1476"], "1473": ["1472", "1470"], "1491": ["1481", "1482"], "1233": ["1234", "1270", "1378"]}, "sentence_to_aspect_id": {"With such a model, I can help more people to learn Frisian language.": ["1489"], "I am trying to train a model that can translate Frisian language documents.": ["1489"], "To do this, I need a parser that uses a corpus of words in sentences with annotation and uses a tagger to assign tags to each word.": ["1492"], "I hope this parser will be designed specifically for the West Frisian language so a Dutch tagger may work well.": ["1473"], "Then, I am looking for some state-of-the-art translation program that can identify the morphological and syntactic structures of the text.": ["1491"], "I need some suggestions for my model to combine the parser and the translation program\u2019s function and generate annotations based on the morphological and syntactic structure of the translated text.": ["1233"]}, "aspect_id_to_sentence": {"1489": ["With such a model, I can help more people to learn Frisian language.", "I am trying to train a model that can translate Frisian language documents."], "1492": ["To do this, I need a parser that uses a corpus of words in sentences with annotation and uses a tagger to assign tags to each word."], "1473": ["I hope this parser will be designed specifically for the West Frisian language so a Dutch tagger may work well."], "1491": ["Then, I am looking for some state-of-the-art translation program that can identify the morphological and syntactic structures of the text."], "1233": ["I need some suggestions for my model to combine the parser and the translation program\u2019s function and generate annotations based on the morphological and syntactic structure of the translated text."]}, "idea_from": [34986], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nWith such a model, I can help more people to learn Frisian language. I am trying to train a model that can translate Frisian language documents. To do this, I need a parser that uses a corpus of words in sentences with annotation and uses a tagger to assign tags to each word. I hope this parser will be designed specifically for the West Frisian language so a Dutch tagger may work well. Then, I am looking for some state-of-the-art translation program that can identify the morphological and syntactic structures of the text. I need some suggestions for my model to combine the parser and the translation program\u2019s function and generate annotations based on the morphological and syntactic structure of the translated text."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"69": [], "469": ["470", "189"], "267": ["257", "62", "461"], "164": [], "466": ["467", "261"]}, "sentence_to_aspect_id": {"I am interested in understanding how modern computer programs can emulate human causal logic.": ["69"], "To achieve this, I would like to learn about the latest computational models that can predict human judgments and compare their performance with that of humans.": ["469"], "Additionally, I am seeking references where human participants are presented with causal structures generated by Bayesian-related models, along with their explanations or feedback based on these model-generated outputs.": ["267"], "I am curious about the most common differences between human decision-making and machine-based decisions, as well as the history of the development of models for causal reasoning, particularly how these models draw inspiration from the neural structures of living creatures.": ["164", "466"]}, "aspect_id_to_sentence": {"69": ["I am interested in understanding how modern computer programs can emulate human causal logic."], "469": ["To achieve this, I would like to learn about the latest computational models that can predict human judgments and compare their performance with that of humans."], "267": ["Additionally, I am seeking references where human participants are presented with causal structures generated by Bayesian-related models, along with their explanations or feedback based on these model-generated outputs."], "164": ["I am curious about the most common differences between human decision-making and machine-based decisions, as well as the history of the development of models for causal reasoning, particularly how these models draw inspiration from the neural structures of living creatures."], "466": ["I am curious about the most common differences between human decision-making and machine-based decisions, as well as the history of the development of models for causal reasoning, particularly how these models draw inspiration from the neural structures of living creatures."]}, "idea_from": [339819], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am interested in understanding how modern computer programs can emulate human causal logic. To achieve this, I would like to learn about the latest computational models that can predict human judgments and compare their performance with that of humans. Additionally, I am seeking references where human participants are presented with causal structures generated by Bayesian-related models, along with their explanations or feedback based on these model-generated outputs. I am curious about the most common differences between human decision-making and machine-based decisions, as well as the history of the development of models for causal reasoning, particularly how these models draw inspiration from the neural structures of living creatures."}


In law enforcement agencies, we need to quickly search through surveillance footage and retrieve videos based on text description or instructions. Usually a lot of people are required to do the job, but now I want to develop a model that can retrieve fine-grained video of actions from videos and match these actions to the text description or instruction. I want to find a model that can embed both video and text description of the actions into a shared embedding space, because then it would be much easier to find out the video of actions based on text description. Also, I hope the model is trained on multiple perspectives for the same action so that the embedding is robust enough. I might also need some techniques to improve the accuracy of cross-modal retrieval if the system’s initial accuracy is not good enough.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"345": ["346", "347", "640"], "316": ["630", "629", "640"], "11": ["346", "596", "594"], "52": ["193", "446", "458"]}, "sentence_to_aspect_id": {"In law enforcement agencies, we need to quickly search through surveillance footage and retrieve videos based on text description or instructions.": ["345"], "Usually a lot of people are required to do the job, but now I want to develop a model that can retrieve fine-grained video of actions from videos and match these actions to the text description or instruction.": ["345"], "I want to find a model that can embed both video and text description of the actions into a shared embedding space, because then it would be much easier to find out the video of actions based on text description.": ["316"], "Also, I hope the model is trained on multiple perspectives for the same action so that the embedding is robust enough.": ["11"], "I might also need some techniques to improve the accuracy of cross-modal retrieval if the system\u2019s initial accuracy is not good enough.": ["52"]}, "aspect_id_to_sentence": {"345": ["In law enforcement agencies, we need to quickly search through surveillance footage and retrieve videos based on text description or instructions.", "Usually a lot of people are required to do the job, but now I want to develop a model that can retrieve fine-grained video of actions from videos and match these actions to the text description or instruction."], "316": ["I want to find a model that can embed both video and text description of the actions into a shared embedding space, because then it would be much easier to find out the video of actions based on text description."], "11": ["Also, I hope the model is trained on multiple perspectives for the same action so that the embedding is robust enough."], "52": ["I might also need some techniques to improve the accuracy of cross-modal retrieval if the system\u2019s initial accuracy is not good enough."]}, "idea_from": [172394], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn law enforcement agencies, we need to quickly search through surveillance footage and retrieve videos based on text description or instructions. Usually a lot of people are required to do the job, but now I want to develop a model that can retrieve fine-grained video of actions from videos and match these actions to the text description or instruction. I want to find a model that can embed both video and text description of the actions into a shared embedding space, because then it would be much easier to find out the video of actions based on text description. Also, I hope the model is trained on multiple perspectives for the same action so that the embedding is robust enough. I might also need some techniques to improve the accuracy of cross-modal retrieval if the system\u2019s initial accuracy is not good enough."}


I want to build a healthcare decision support system that will recommend treatment plans for patients. In order to make the prediction process transparent and interpretable, I believe I could use reinforcement learning. Given the patient’s past medical history, I will spawn many processes, in each process an artificial or digital twin for the patient is spawned to mimic the patient’s health trajectories. In each process, the patient will receive many treatments, prescriptions, surgeries, and will experience a rest and recovery period, and eventually some form of complications or disease will terminate the patient, and thus terminate the process as well. I want to model this entire environment dynamic using reinforcement learning, conditioned on the patient's past medical record. I will model each dynamic in the environment as some form of numeric optimization problems where the human body strives to optimize its ability to recover and detect possible illness. I need the entire framework to be effective in casting optimization problems, and efficiently solve numeric planning problems involving obstacles. I will use this modeling approach to determine the best course of treatment for the patient to maximize the patient’s life expectancy.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"298": ["386", "242", "246"], "268": ["246", "83", "276"], "1462": ["246", "1440", "417", "648"], "1392": ["246", "83", "402"], "205": ["206", "296", "463"]}, "sentence_to_aspect_id": {"I want to build a healthcare decision support system that will recommend treatment plans for patients.": ["298"], "In order to make the prediction process transparent and interpretable, I believe I could use reinforcement learning.": ["268"], "Given the patient\u2019s past medical history, I will spawn many processes, in each process an artificial or digital twin for the patient is spawned to mimic the patient\u2019s health trajectories.": ["1462"], "In each process, the patient will receive many treatments, prescriptions, surgeries, and will experience a rest and recovery period, and eventually some form of complications or disease will terminate the patient, and thus terminate the process as well.": ["1462"], "I want to model this entire environment dynamic using reinforcement learning, conditioned on the patient's past medical record.": ["1392"], "I will model each dynamic in the environment as some form of numeric optimization problems where the human body strives to optimize its ability to recover and detect possible illness.": ["205"], "I need the entire framework to be effective in casting optimization problems, and efficiently solve numeric planning problems involving obstacles.": ["205"], "I will use this modeling approach to determine the best course of treatment for the patient to maximize the patient\u2019s life expectancy.": ["298"]}, "aspect_id_to_sentence": {"298": ["I want to build a healthcare decision support system that will recommend treatment plans for patients.", "I will use this modeling approach to determine the best course of treatment for the patient to maximize the patient\u2019s life expectancy."], "268": ["In order to make the prediction process transparent and interpretable, I believe I could use reinforcement learning."], "1462": ["Given the patient\u2019s past medical history, I will spawn many processes, in each process an artificial or digital twin for the patient is spawned to mimic the patient\u2019s health trajectories.", "In each process, the patient will receive many treatments, prescriptions, surgeries, and will experience a rest and recovery period, and eventually some form of complications or disease will terminate the patient, and thus terminate the process as well."], "1392": ["I want to model this entire environment dynamic using reinforcement learning, conditioned on the patient's past medical record."], "205": ["I will model each dynamic in the environment as some form of numeric optimization problems where the human body strives to optimize its ability to recover and detect possible illness.", "I need the entire framework to be effective in casting optimization problems, and efficiently solve numeric planning problems involving obstacles."]}, "idea_from": [160421, 15151], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want to build a healthcare decision support system that will recommend treatment plans for patients. In order to make the prediction process transparent and interpretable, I believe I could use reinforcement learning. Given the patient\u2019s past medical history, I will spawn many processes, in each process an artificial or digital twin for the patient is spawned to mimic the patient\u2019s health trajectories. In each process, the patient will receive many treatments, prescriptions, surgeries, and will experience a rest and recovery period, and eventually some form of complications or disease will terminate the patient, and thus terminate the process as well. I want to model this entire environment dynamic using reinforcement learning, conditioned on the patient's past medical record. I will model each dynamic in the environment as some form of numeric optimization problems where the human body strives to optimize its ability to recover and detect possible illness. I need the entire framework to be effective in casting optimization problems, and efficiently solve numeric planning problems involving obstacles. I will use this modeling approach to determine the best course of treatment for the patient to maximize the patient\u2019s life expectancy."}


I am a medical researcher working towards Melanoma Detection. My dataset consists of patients’ skin images. I want to build a system that can automatically find the regions where melanoma is likely to occur. Skin lesions are usually places where melanoma would occur, but most areas of skin lesions do not cause melanoma, so I really need to pinpoint the exact locations where melanoma could occur. Another difficulty is that I don’t have many labeled images that show exactly where the melanoma occurs. Most images only have a binary label. How do I first find all the skin lesions and then pinpoint the location of the melanoma without having finely annotated data?  My current thinking is to use clustering for finding skin lesions and use some segmentation technique to find melanoma location. Finally, the model needs to be lightweight and not use too many parameters.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"244": ["185", "485", "453"], "1393": ["185", "614", "473"], "106": ["700", "614"], "105": ["705", "613"], "521": ["185", "588"]}, "sentence_to_aspect_id": {"I am a medical researcher working towards Melanoma Detection.": ["244"], "My dataset consists of patients\u2019 skin images.": ["244"], "I want to build a system that can automatically find the regions where melanoma is likely to occur.": ["244", "1393"], "Skin lesions are usually places where melanoma would occur, but most areas of skin lesions do not cause melanoma, so I really need to pinpoint the exact locations where melanoma could occur.": ["1393"], "Another difficulty is that I don\u2019t have many labeled images that show exactly where the melanoma occurs.": ["1393"], "Most images only have a binary label.": ["1393"], "How do I first find all the skin lesions and then pinpoint the location of the melanoma without having finely annotated data?": ["1393"], "My current thinking is to use clustering for finding skin lesions and use some segmentation technique to find melanoma location.": ["106", "105"], "Finally, the model needs to be lightweight and not use too many parameters.": ["521"]}, "aspect_id_to_sentence": {"244": ["I am a medical researcher working towards Melanoma Detection.", "My dataset consists of patients\u2019 skin images.", "I want to build a system that can automatically find the regions where melanoma is likely to occur."], "1393": ["I want to build a system that can automatically find the regions where melanoma is likely to occur.", "Skin lesions are usually places where melanoma would occur, but most areas of skin lesions do not cause melanoma, so I really need to pinpoint the exact locations where melanoma could occur.", "Another difficulty is that I don\u2019t have many labeled images that show exactly where the melanoma occurs.", "Most images only have a binary label.", "How do I first find all the skin lesions and then pinpoint the location of the melanoma without having finely annotated data?"], "106": ["My current thinking is to use clustering for finding skin lesions and use some segmentation technique to find melanoma location."], "105": ["My current thinking is to use clustering for finding skin lesions and use some segmentation technique to find melanoma location."], "521": ["Finally, the model needs to be lightweight and not use too many parameters."]}, "idea_from": [131575], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am a medical researcher working towards Melanoma Detection. My dataset consists of patients\u2019 skin images. I want to build a system that can automatically find the regions where melanoma is likely to occur. Skin lesions are usually places where melanoma would occur, but most areas of skin lesions do not cause melanoma, so I really need to pinpoint the exact locations where melanoma could occur. Another difficulty is that I don\u2019t have many labeled images that show exactly where the melanoma occurs. Most images only have a binary label. How do I first find all the skin lesions and then pinpoint the location of the melanoma without having finely annotated data?  My current thinking is to use clustering for finding skin lesions and use some segmentation technique to find melanoma location. Finally, the model needs to be lightweight and not use too many parameters."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"66": ["413", "338"], "555": ["532", "582"], "301": ["413", "78"], "439": ["413", "142"], "493": ["235", "403", "337"], "356": ["413", "355", "14"]}, "sentence_to_aspect_id": {"I am seeking to develop a robotic agent capable of comprehending user input and completing assigned tasks.": ["66"], "I believe the agent's functionality can be separated into two segments: understanding commands and executing actions.": ["66"], "First, I require a model that can interpret user commands, whether written or verbal, and translate them into a list of real-world actions.": ["555"], "I believe a language model would be beneficial at this stage.": ["301"], "However, I need advice on how to strike a balance between the size of the language model and the accuracy of its interpretation of the input.": ["439"], "Additionally, I require a reinforcement learning framework for the action execution phase.": ["493"], "However, I've noticed that users often provide detailed yet suboptimal instructions.": ["493"], "Therefore, I am in search of a reward function that incorporates social psychology to enhance service quality.": ["493"], "I anticipate an algorithm that empowers the agent to assess user input and carry out the most efficient actions while not straying too far from the original input.": ["356"]}, "aspect_id_to_sentence": {"66": ["I am seeking to develop a robotic agent capable of comprehending user input and completing assigned tasks.", "I believe the agent's functionality can be separated into two segments: understanding commands and executing actions."], "555": ["First, I require a model that can interpret user commands, whether written or verbal, and translate them into a list of real-world actions."], "301": ["I believe a language model would be beneficial at this stage."], "439": ["However, I need advice on how to strike a balance between the size of the language model and the accuracy of its interpretation of the input."], "493": ["Additionally, I require a reinforcement learning framework for the action execution phase.", "However, I've noticed that users often provide detailed yet suboptimal instructions.", "Therefore, I am in search of a reward function that incorporates social psychology to enhance service quality."], "356": ["I anticipate an algorithm that empowers the agent to assess user input and carry out the most efficient actions while not straying too far from the original input."]}, "idea_from": [243197], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am seeking to develop a robotic agent capable of comprehending user input and completing assigned tasks. I believe the agent's functionality can be separated into two segments: understanding commands and executing actions. First, I require a model that can interpret user commands, whether written or verbal, and translate them into a list of real-world actions. I believe a language model would be beneficial at this stage. However, I need advice on how to strike a balance between the size of the language model and the accuracy of its interpretation of the input. Additionally, I require a reinforcement learning framework for the action execution phase. However, I've noticed that users often provide detailed yet suboptimal instructions. Therefore, I am in search of a reward function that incorporates social psychology to enhance service quality. I anticipate an algorithm that empowers the agent to assess user input and carry out the most efficient actions while not straying too far from the original input."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"342": ["1403", "1406"], "310": ["1403", "350", "419", "253", "94"], "238": ["94", "175", "603", "680"], "167": []}, "sentence_to_aspect_id": {"I aim to develop a comprehensive fake news detection system capable of identifying and curbing the spread of misinformation.": ["342"], "The system should be adaptable enough to detect fake news across various domains, such as tracking misinformation related to COVID-19 on social media platforms.": ["342"], "Given the multimedia nature of news, the system should incorporate an automatic speech recognition module, an image (video) processing module, and a language model, among other potential components.": ["310"], "It would be beneficial to convert audio recognition into transcripts and image and video processing into natural language descriptions, necessitating the design of a robust language model.": ["238"], "Additionally, I am keen to explore the dynamics of large-scale information dissemination, which I hope to address by my proposed fake news detection system.": ["167"]}, "aspect_id_to_sentence": {"342": ["I aim to develop a comprehensive fake news detection system capable of identifying and curbing the spread of misinformation.", "The system should be adaptable enough to detect fake news across various domains, such as tracking misinformation related to COVID-19 on social media platforms."], "310": ["Given the multimedia nature of news, the system should incorporate an automatic speech recognition module, an image (video) processing module, and a language model, among other potential components."], "238": ["It would be beneficial to convert audio recognition into transcripts and image and video processing into natural language descriptions, necessitating the design of a robust language model."], "167": ["Additionally, I am keen to explore the dynamics of large-scale information dissemination, which I hope to address by my proposed fake news detection system."]}, "idea_from": [221967, 270687, 76125], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI aim to develop a comprehensive fake news detection system capable of identifying and curbing the spread of misinformation. The system should be adaptable enough to detect fake news across various domains, such as tracking misinformation related to COVID-19 on social media platforms. Given the multimedia nature of news, the system should incorporate an automatic speech recognition module, an image (video) processing module, and a language model, among other potential components. It would be beneficial to convert audio recognition into transcripts and image and video processing into natural language descriptions, necessitating the design of a robust language model. Additionally, I am keen to explore the dynamics of large-scale information dissemination, which I hope to address by my proposed fake news detection system."}


Though data augmentation can be effective to improve model's performances, for natural language models, data augmentation can also cause noises in model’s output such as prediction or machine translation. My research fields involve training models for text classification, question answering and sequence labeling, all of which could benefit from using augmented dataset. It is important for me to understand how data augmentations could cause noises in predictions. I want to identify what augmentation technique could cause noises in prediction, so I should perform a sensitivity analysis. Overall, I want to penalize the model if there are too much noises in the output due to data augmentation. My current idea is to use some form of consistency regularization in my loss function during training or fine-tuning. In the end, I want to see improved performance of my trained models with consistency regularization on benchmark datasets for NLP tasks.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1359": ["1189", "797", "1030"], "1167": [], "1258": ["1259", "1035"], "1033": ["1189", "1259", "1034"], "1376": [], "1028": ["1189", "1259", "1376", "1029"]}, "sentence_to_aspect_id": {"Though data augmentation can be effective to improve model's performances, for natural language models, data augmentation can also cause noises in model\u2019s output such as prediction or machine translation.": ["1359"], "My research fields involve training models for text classification, question answering and sequence labeling, all of which could benefit from using augmented dataset.": ["1167"], "It is important for me to understand how data augmentations could cause noises in predictions.": ["1359", "1258"], "I want to identify what augmentation technique could cause noises in prediction, so I should perform a sensitivity analysis.": ["1258"], "Overall, I want to penalize the model if there are too much noises in the output due to data augmentation.": ["1033"], "My current idea is to use some form of consistency regularization in my loss function during training or fine-tuning.": ["1376"], "In the end, I want to see improved performance of my trained models with consistency regularization on benchmark datasets for NLP tasks.": ["1028"]}, "aspect_id_to_sentence": {"1359": ["Though data augmentation can be effective to improve model's performances, for natural language models, data augmentation can also cause noises in model\u2019s output such as prediction or machine translation.", "It is important for me to understand how data augmentations could cause noises in predictions."], "1167": ["My research fields involve training models for text classification, question answering and sequence labeling, all of which could benefit from using augmented dataset."], "1258": ["It is important for me to understand how data augmentations could cause noises in predictions.", "I want to identify what augmentation technique could cause noises in prediction, so I should perform a sensitivity analysis."], "1033": ["Overall, I want to penalize the model if there are too much noises in the output due to data augmentation."], "1376": ["My current idea is to use some form of consistency regularization in my loss function during training or fine-tuning."], "1028": ["In the end, I want to see improved performance of my trained models with consistency regularization on benchmark datasets for NLP tasks."]}, "idea_from": [41912], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nThough data augmentation can be effective to improve model's performances, for natural language models, data augmentation can also cause noises in model\u2019s output such as prediction or machine translation. My research fields involve training models for text classification, question answering and sequence labeling, all of which could benefit from using augmented dataset. It is important for me to understand how data augmentations could cause noises in predictions. I want to identify what augmentation technique could cause noises in prediction, so I should perform a sensitivity analysis. Overall, I want to penalize the model if there are too much noises in the output due to data augmentation. My current idea is to use some form of consistency regularization in my loss function during training or fine-tuning. In the end, I want to see improved performance of my trained models with consistency regularization on benchmark datasets for NLP tasks."}


My goal is to develop a deep neural network agent that can navigate in diverse, unseen environments where changes in objects, textures, colors, and noises introduced naturally from faulty devices and signal interferences are common. In a straightforward manner, I could annotate a diverse set of fully labeled objects, textures and scene layouts, all of which are contaminated by noises to some extent. Then the agent will be trained with varying levels of augmentations. Moreover, I would like my model to be structured enough so that I could interpret the effects of introducing noises and transforms to the model’s input. What are some architectural designs for my model? In addition, I want my model to recognize geometric invariants in images even if they are corrupted by noises and perturbed by circumstances. In the end, my agent needs to be robust against all these perturbations and noises. A real-life scenario could involve developing an autonomous driving system that can navigate safely in diverse, challenging environments.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"116": ["131", "107", "387", "445", "451"], "764": ["107", "623"], "124": ["107", "111", "102", "104"], "683": ["684", "423"]}, "sentence_to_aspect_id": {"My goal is to develop a deep neural network agent that can navigate in diverse, unseen environments where changes in objects, textures, colors, and noises introduced naturally from faulty devices and signal interferences are common.": ["116"], "In a straightforward manner, I could annotate a diverse set of fully labeled objects, textures and scene layouts, all of which are contaminated by noises to some extent.": ["116", "764"], "Then the agent will be trained with varying levels of augmentations.": ["764"], "Moreover, I would like my model to be structured enough so that I could interpret the effects of introducing noises and transforms to the model\u2019s input.": ["124"], "What are some architectural designs for my model?": ["124"], "In addition, I want my model to recognize geometric invariants in images even if they are corrupted by noises and perturbed by circumstances.": ["683"], "In the end, my agent needs to be robust against all these perturbations and noises.": ["116"], "A real-life scenario could involve developing an autonomous driving system that can navigate safely in diverse, challenging environments.": ["116"]}, "aspect_id_to_sentence": {"116": ["My goal is to develop a deep neural network agent that can navigate in diverse, unseen environments where changes in objects, textures, colors, and noises introduced naturally from faulty devices and signal interferences are common.", "In a straightforward manner, I could annotate a diverse set of fully labeled objects, textures and scene layouts, all of which are contaminated by noises to some extent.", "In the end, my agent needs to be robust against all these perturbations and noises.", "A real-life scenario could involve developing an autonomous driving system that can navigate safely in diverse, challenging environments."], "764": ["In a straightforward manner, I could annotate a diverse set of fully labeled objects, textures and scene layouts, all of which are contaminated by noises to some extent.", "Then the agent will be trained with varying levels of augmentations."], "124": ["Moreover, I would like my model to be structured enough so that I could interpret the effects of introducing noises and transforms to the model\u2019s input.", "What are some architectural designs for my model?"], "683": ["In addition, I want my model to recognize geometric invariants in images even if they are corrupted by noises and perturbed by circumstances."]}, "idea_from": [245731, 271695], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nMy goal is to develop a deep neural network agent that can navigate in diverse, unseen environments where changes in objects, textures, colors, and noises introduced naturally from faulty devices and signal interferences are common. In a straightforward manner, I could annotate a diverse set of fully labeled objects, textures and scene layouts, all of which are contaminated by noises to some extent. Then the agent will be trained with varying levels of augmentations. Moreover, I would like my model to be structured enough so that I could interpret the effects of introducing noises and transforms to the model\u2019s input. What are some architectural designs for my model? In addition, I want my model to recognize geometric invariants in images even if they are corrupted by noises and perturbed by circumstances. In the end, my agent needs to be robust against all these perturbations and noises. A real-life scenario could involve developing an autonomous driving system that can navigate safely in diverse, challenging environments."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1107": [], "1046": ["1043", "1044"], "942": ["1108", "1109"], "1041": ["1042", "955"], "1027": ["1387", "845"], "780": ["982", "1031"], "1080": ["1081", "877"]}, "sentence_to_aspect_id": {"In natural language processing tasks, it is essential to accurately represent the meanings of words to achieve optimal performance.": ["1107"], "However, this task is challenging due to the potential for multiple interpretations of a single word.": [], "As a solution, I propose a method that initially learns to identify the smallest unambiguous phrase component within sentences and paragraphs.": ["1046"], "Following this, I aim to accurately model their latent vectorized representations, taking into account their semantic and structural relationships with neighboring semantic components.": ["942"], "My current approach involves capturing the semantic relationship using a pretraining method akin to word2vec.": ["1041"], "To encapsulate the structural syntactic relationships, I propose modeling each semantic component as a node, with the entire paragraph represented as a sparse graph structure.": ["1027"], "Subsequently, specialized graph-based neural network algorithms can be applied to obtain latent structural representations.": ["780"], "Ultimately, my proposed embedding model should assign a more precise embedding to each text input, potentially enhancing performance in numerous downstream tasks.": ["1080"]}, "aspect_id_to_sentence": {"1107": ["In natural language processing tasks, it is essential to accurately represent the meanings of words to achieve optimal performance."], "1046": ["As a solution, I propose a method that initially learns to identify the smallest unambiguous phrase component within sentences and paragraphs."], "942": ["Following this, I aim to accurately model their latent vectorized representations, taking into account their semantic and structural relationships with neighboring semantic components."], "1041": ["My current approach involves capturing the semantic relationship using a pretraining method akin to word2vec."], "1027": ["To encapsulate the structural syntactic relationships, I propose modeling each semantic component as a node, with the entire paragraph represented as a sparse graph structure."], "780": ["Subsequently, specialized graph-based neural network algorithms can be applied to obtain latent structural representations."], "1080": ["Ultimately, my proposed embedding model should assign a more precise embedding to each text input, potentially enhancing performance in numerous downstream tasks."]}, "idea_from": [226692], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn natural language processing tasks, it is essential to accurately represent the meanings of words to achieve optimal performance. However, this task is challenging due to the potential for multiple interpretations of a single word. As a solution, I propose a method that initially learns to identify the smallest unambiguous phrase component within sentences and paragraphs. Following this, I aim to accurately model their latent vectorized representations, taking into account their semantic and structural relationships with neighboring semantic components. My current approach involves capturing the semantic relationship using a pretraining method akin to word2vec. To encapsulate the structural syntactic relationships, I propose modeling each semantic component as a node, with the entire paragraph represented as a sparse graph structure. Subsequently, specialized graph-based neural network algorithms can be applied to obtain latent structural representations. Ultimately, my proposed embedding model should assign a more precise embedding to each text input, potentially enhancing performance in numerous downstream tasks."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"220": ["75", "113"], "165": ["375", "444"], "497": [], "357": ["75", "324"], "479": ["75", "286"], "99": [], "98": ["75", "264"]}, "sentence_to_aspect_id": {"I am currently engaged in a project that involves compressing High Dynamic Range (HDR) images to be compatible with older JPEG formats.": ["220"], "I have found that the existing JPEG XT standard, which is designed to support high dynamic range imaging, does not fully accommodate HDR images.": ["220"], "Consequently, I am in need of a model that can convert HDR images into a JPEG XT version with minimal loss of information.": ["165"], "I am interested in learning how to incorporate image compression methods into this task.": ["497"], "My objective is to develop a universal model that can effectively handle this task without the need to find the optimal setting for each individual image.": ["357"], "I am seeking guidance on how to design and train such a model, including the selection of appropriate training data and suitable performance metrics.": ["479"], "I anticipate that a neural network-based compression algorithm could potentially outperform traditional algorithms.": ["99"], "Therefore, I would appreciate any insights into the compression methods based on CNN, autoencoders, or GANs.": ["98"]}, "aspect_id_to_sentence": {"220": ["I am currently engaged in a project that involves compressing High Dynamic Range (HDR) images to be compatible with older JPEG formats.", "I have found that the existing JPEG XT standard, which is designed to support high dynamic range imaging, does not fully accommodate HDR images."], "165": ["Consequently, I am in need of a model that can convert HDR images into a JPEG XT version with minimal loss of information."], "497": ["I am interested in learning how to incorporate image compression methods into this task."], "357": ["My objective is to develop a universal model that can effectively handle this task without the need to find the optimal setting for each individual image."], "479": ["I am seeking guidance on how to design and train such a model, including the selection of appropriate training data and suitable performance metrics."], "99": ["I anticipate that a neural network-based compression algorithm could potentially outperform traditional algorithms."], "98": ["Therefore, I would appreciate any insights into the compression methods based on CNN, autoencoders, or GANs."]}, "idea_from": [226538], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am currently engaged in a project that involves compressing High Dynamic Range (HDR) images to be compatible with older JPEG formats. I have found that the existing JPEG XT standard, which is designed to support high dynamic range imaging, does not fully accommodate HDR images. Consequently, I am in need of a model that can convert HDR images into a JPEG XT version with minimal loss of information. I am interested in learning how to incorporate image compression methods into this task. My objective is to develop a universal model that can effectively handle this task without the need to find the optimal setting for each individual image. I am seeking guidance on how to design and train such a model, including the selection of appropriate training data and suitable performance metrics. I anticipate that a neural network-based compression algorithm could potentially outperform traditional algorithms. Therefore, I would appreciate any insights into the compression methods based on CNN, autoencoders, or GANs."}


I want to build a customer service chatbot that can understand and respond to complex inquiries from customers. For my purposes, I assume the complexity in inquiries come from having multiple intents and slots in a single utterance. Therefore I need a model that can detect intent and fill slots, jointly. The model needs to have fast inference speed, and need to avoid introducing unseen data when restructuring the customer’s inquiry. Therefore I should look into how to make my model faster during inference and avoid information leakage. My current idea is to leverage the interaction between multiple intents and slots in an utterance. These intents and slots are unlabelled, which makes them uncoordinated and more challenging. Possibly I could try some form of graphical neural network. Overall, I want to beat state of the art systems in this task in both speed and accuracy.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1170": ["1174", "1292", "1306"], "807": [], "908": ["914", "915"], "1350": [], "1228": ["1174", "1292", "1227", "1226"], "1380": [], "808": [], "1377": [], "917": []}, "sentence_to_aspect_id": {"I want to build a customer service chatbot that can understand and respond to complex inquiries from customers.": ["1170"], "For my purposes, I assume the complexity in inquiries come from having multiple intents and slots in a single utterance.": ["807"], "Therefore I need a model that can detect intent and fill slots, jointly.": ["908"], "The model needs to have fast inference speed, and need to avoid introducing unseen data when restructuring the customer\u2019s inquiry.": ["1350", "1228"], "Therefore I should look into how to make my model faster during inference and avoid information leakage.": ["1350", "1228"], "My current idea is to leverage the interaction between multiple intents and slots in an utterance.": ["1380"], "These intents and slots are unlabelled, which makes them uncoordinated and more challenging.": ["808"], "Possibly I could try some form of graphical neural network.": ["1377"], "Overall, I want to beat state of the art systems in this task in both speed and accuracy.": ["917"]}, "aspect_id_to_sentence": {"1170": ["I want to build a customer service chatbot that can understand and respond to complex inquiries from customers."], "807": ["For my purposes, I assume the complexity in inquiries come from having multiple intents and slots in a single utterance."], "908": ["Therefore I need a model that can detect intent and fill slots, jointly."], "1350": ["The model needs to have fast inference speed, and need to avoid introducing unseen data when restructuring the customer\u2019s inquiry.", "Therefore I should look into how to make my model faster during inference and avoid information leakage."], "1228": ["The model needs to have fast inference speed, and need to avoid introducing unseen data when restructuring the customer\u2019s inquiry.", "Therefore I should look into how to make my model faster during inference and avoid information leakage."], "1380": ["My current idea is to leverage the interaction between multiple intents and slots in an utterance."], "808": ["These intents and slots are unlabelled, which makes them uncoordinated and more challenging."], "1377": ["Possibly I could try some form of graphical neural network."], "917": ["Overall, I want to beat state of the art systems in this task in both speed and accuracy."]}, "idea_from": [45038], "num_aspects": 9, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want to build a customer service chatbot that can understand and respond to complex inquiries from customers. For my purposes, I assume the complexity in inquiries come from having multiple intents and slots in a single utterance. Therefore I need a model that can detect intent and fill slots, jointly. The model needs to have fast inference speed, and need to avoid introducing unseen data when restructuring the customer\u2019s inquiry. Therefore I should look into how to make my model faster during inference and avoid information leakage. My current idea is to leverage the interaction between multiple intents and slots in an utterance. These intents and slots are unlabelled, which makes them uncoordinated and more challenging. Possibly I could try some form of graphical neural network. Overall, I want to beat state of the art systems in this task in both speed and accuracy."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1471": ["1483", "1469"], "761": [], "1477": ["1478", "37", "36"], "1495": ["1478", "1494", "1493"], "1485": ["1486", "1487"], "1468": ["1483", "1486", "1479"], "1480": ["1483", "1469", "1484"]}, "sentence_to_aspect_id": {"I am working on a project where I input a sentence, my system will return related images based on my input sentence.": ["1471"], "I call this task image retrieval from sentences.": ["1471"], "This system needs to work with different languages.": ["761"], "My current plan is to first create a synthetic dataset that consists of tuples of sentences in different languages and an image.": ["1477"], "I am trying to use image captioning or image annotation pre-trained models to create this synthetic dataset.": ["1495"], "After we have created this synthetic dataset, I will build a model that can learn the representations of sentences with respect to the image.": ["1485"], "I believe my work is in the intersection of information retrieval, learning sentence representation and data augmentation.": ["1468"], "In the end, I want my work to be integrated with a search engine that can be deployed in the cloud.": ["1480"]}, "aspect_id_to_sentence": {"1471": ["I am working on a project where I input a sentence, my system will return related images based on my input sentence.", "I call this task image retrieval from sentences."], "761": ["This system needs to work with different languages."], "1477": ["My current plan is to first create a synthetic dataset that consists of tuples of sentences in different languages and an image."], "1495": ["I am trying to use image captioning or image annotation pre-trained models to create this synthetic dataset."], "1485": ["After we have created this synthetic dataset, I will build a model that can learn the representations of sentences with respect to the image."], "1468": ["I believe my work is in the intersection of information retrieval, learning sentence representation and data augmentation."], "1480": ["In the end, I want my work to be integrated with a search engine that can be deployed in the cloud."]}, "idea_from": [157257], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am working on a project where I input a sentence, my system will return related images based on my input sentence. I call this task image retrieval from sentences. This system needs to work with different languages. My current plan is to first create a synthetic dataset that consists of tuples of sentences in different languages and an image. I am trying to use image captioning or image annotation pre-trained models to create this synthetic dataset. After we have created this synthetic dataset, I will build a model that can learn the representations of sentences with respect to the image. I believe my work is in the intersection of information retrieval, learning sentence representation and data augmentation. In the end, I want my work to be integrated with a search engine that can be deployed in the cloud."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"226": ["227", "159", "265"], "230": ["231", "224"], "21": ["227", "719"], "266": [], "416": ["56", "54", "50"], "47": [], "272": ["46", "367", "271"]}, "sentence_to_aspect_id": {"I find that when I need to train a NLP model for a task such as speech recognition, I usually need multiple human annotations per example and I need to aggregate them in order to get some ground-truth.": ["226"], "To reduce the need for human annotation and aggregation, I plan to develop an automated model that can produce aggregated annotation and I will use this improved annotated dataset to train more accurate machine learning models.": ["226", "230"], "To achieve this, I plan on using different automatic annotation toolkits and make multiple annotations for each example.": ["230"], "I also need to take into account the difficulties of examples and I need to learn some representations of each annotation tool (because some may be better than the others).": ["21", "266"], "I would like the annotation procedure to be integrated with the training of machine learning models, so that the entire framework is end-to-end.": ["416"], "To achieve this, I would like to explore different aggregation methods.": ["47"], "Because we are taking multiple annotations per example, there are likely some redundancies, so what would be the best way to guarantee annotation accuracy while reducing annotation redundancy (in other words, taking as few annotations as possible)?": ["272"]}, "aspect_id_to_sentence": {"226": ["I find that when I need to train a NLP model for a task such as speech recognition, I usually need multiple human annotations per example and I need to aggregate them in order to get some ground-truth.", "To reduce the need for human annotation and aggregation, I plan to develop an automated model that can produce aggregated annotation and I will use this improved annotated dataset to train more accurate machine learning models."], "230": ["To reduce the need for human annotation and aggregation, I plan to develop an automated model that can produce aggregated annotation and I will use this improved annotated dataset to train more accurate machine learning models.", "To achieve this, I plan on using different automatic annotation toolkits and make multiple annotations for each example."], "21": ["I also need to take into account the difficulties of examples and I need to learn some representations of each annotation tool (because some may be better than the others)."], "266": ["I also need to take into account the difficulties of examples and I need to learn some representations of each annotation tool (because some may be better than the others)."], "416": ["I would like the annotation procedure to be integrated with the training of machine learning models, so that the entire framework is end-to-end."], "47": ["To achieve this, I would like to explore different aggregation methods."], "272": ["Because we are taking multiple annotations per example, there are likely some redundancies, so what would be the best way to guarantee annotation accuracy while reducing annotation redundancy (in other words, taking as few annotations as possible)?"]}, "idea_from": [132478], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI find that when I need to train a NLP model for a task such as speech recognition, I usually need multiple human annotations per example and I need to aggregate them in order to get some ground-truth. To reduce the need for human annotation and aggregation, I plan to develop an automated model that can produce aggregated annotation and I will use this improved annotated dataset to train more accurate machine learning models. To achieve this, I plan on using different automatic annotation toolkits and make multiple annotations for each example. I also need to take into account the difficulties of examples and I need to learn some representations of each annotation tool (because some may be better than the others). I would like the annotation procedure to be integrated with the training of machine learning models, so that the entire framework is end-to-end. To achieve this, I would like to explore different aggregation methods. Because we are taking multiple annotations per example, there are likely some redundancies, so what would be the best way to guarantee annotation accuracy while reducing annotation redundancy (in other words, taking as few annotations as possible)? "}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"101": ["198", "330", "110", "407"], "1402": ["198", "1414", "1439", "1437"], "297": ["198", "204", "202", "203"], "507": ["198", "1430", "1425"], "508": ["198", "1418", "1413", "1423"]}, "sentence_to_aspect_id": {"As a family member of a border control officer, I want to make his life easier.": [], "I require a facial recognition system that utilizes Deep Neural Networks to verify identities from images that can contain camouflaged people .": ["101"], "For example, some people may wear a wig or cosmetic contact lenses when taking photos at the border control.": ["101"], "The system must be automated and provide transparency in decision-making, presenting information in a way that is easily understood.": ["1402"], "I need a framework to evaluate the quality of the method, including its ability to detect and remove artifacts, and analyze their influence on decision-making.": ["297"], "The system must be able to identify uncertain or incorrect decisions and adjust its highlighting method accordingly.": ["507"], "For example, when processing a large number of travelers at a busy airport, I need a reliable and efficient system that can quickly and accurately verify identities and detect any potential security threats.": ["508"]}, "aspect_id_to_sentence": {"101": ["I require a facial recognition system that utilizes Deep Neural Networks to verify identities from images that can contain camouflaged people .", "For example, some people may wear a wig or cosmetic contact lenses when taking photos at the border control."], "1402": ["The system must be automated and provide transparency in decision-making, presenting information in a way that is easily understood."], "297": ["I need a framework to evaluate the quality of the method, including its ability to detect and remove artifacts, and analyze their influence on decision-making."], "507": ["The system must be able to identify uncertain or incorrect decisions and adjust its highlighting method accordingly."], "508": ["For example, when processing a large number of travelers at a busy airport, I need a reliable and efficient system that can quickly and accurately verify identities and detect any potential security threats."]}, "idea_from": [60321], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nAs a family member of a border control officer, I want to make his life easier. I require a facial recognition system that utilizes Deep Neural Networks to verify identities from images that can contain camouflaged people . For example, some people may wear a wig or cosmetic contact lenses when taking photos at the border control. The system must be automated and provide transparency in decision-making, presenting information in a way that is easily understood. I need a framework to evaluate the quality of the method, including its ability to detect and remove artifacts, and analyze their influence on decision-making. The system must be able to identify uncertain or incorrect decisions and adjust its highlighting method accordingly. For example, when processing a large number of travelers at a busy airport, I need a reliable and efficient system that can quickly and accurately verify identities and detect any potential security threats."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"170": ["155", "171", "151"], "368": ["447", "273", "361"], "187": [], "489": ["488", "281"], "234": ["273", "209", "459", "248"], "404": ["273", "126"], "487": []}, "sentence_to_aspect_id": {"I want to improve the interpretability and explainability of machine learning models in various fields such as healthcare and finance by using representation learning.": ["170"], "Since the learned representations could be highly related in certain scenarios, I want to disentangle these representations so that they are no longer related, and we can clearly see the aspects or features they are representing.": ["368"], "I need to first identify different metrics for representation learning, as well as some existing symmetry-based representation disentanglement techniques.": ["187", "489"], "Furthermore, I want to build this procedure under a mathematical framework so that it is precise and has theoretical guarantees.": ["234"], "For the sake of simplicity, this procedure should induce a simple linear representation.": ["404"], "I also need a way to check whether existing models have the ability to induce linear disentangled representation.": ["487"]}, "aspect_id_to_sentence": {"170": ["I want to improve the interpretability and explainability of machine learning models in various fields such as healthcare and finance by using representation learning."], "368": ["Since the learned representations could be highly related in certain scenarios, I want to disentangle these representations so that they are no longer related, and we can clearly see the aspects or features they are representing."], "187": ["I need to first identify different metrics for representation learning, as well as some existing symmetry-based representation disentanglement techniques."], "489": ["I need to first identify different metrics for representation learning, as well as some existing symmetry-based representation disentanglement techniques."], "234": ["Furthermore, I want to build this procedure under a mathematical framework so that it is precise and has theoretical guarantees."], "404": ["For the sake of simplicity, this procedure should induce a simple linear representation."], "487": ["I also need a way to check whether existing models have the ability to induce linear disentangled representation."]}, "idea_from": [105383], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want to improve the interpretability and explainability of machine learning models in various fields such as healthcare and finance by using representation learning. Since the learned representations could be highly related in certain scenarios, I want to disentangle these representations so that they are no longer related, and we can clearly see the aspects or features they are representing. I need to first identify different metrics for representation learning, as well as some existing symmetry-based representation disentanglement techniques. Furthermore, I want to build this procedure under a mathematical framework so that it is precise and has theoretical guarantees. For the sake of simplicity, this procedure should induce a simple linear representation. I also need a way to check whether existing models have the ability to induce linear disentangled representation."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1099": ["1180", "1119", "1284"], "1279": ["1280", "1291", "1284"], "897": [], "987": ["1281", "1252", "1284"], "911": ["912", "910"]}, "sentence_to_aspect_id": {"My goal is to build a conversational chatbot that can generate full-length natural language answers to spoken questions.": ["1099"], "Because conversations happen in real time, In order to quickly detect the intents in the spoken questions, I do not want to use large scale neural language models.": ["1279", "897"], "Instead, I want to utilize heuristics similar to  the word saliency scores which can quickly help the model identify key concepts and intents in a spoken question.": ["987"], "What are some ways to calculate word saliency scores?": ["987"], "After the intents of the spoken question have been established, the model must be able to retrieve facts from a knowledge base, which typically is a graph.": ["911"], "Therefore, the natural language model needs to take a graph as input.": ["911"]}, "aspect_id_to_sentence": {"1099": ["My goal is to build a conversational chatbot that can generate full-length natural language answers to spoken questions."], "1279": ["Because conversations happen in real time, In order to quickly detect the intents in the spoken questions, I do not want to use large scale neural language models."], "897": ["Because conversations happen in real time, In order to quickly detect the intents in the spoken questions, I do not want to use large scale neural language models."], "987": ["Instead, I want to utilize heuristics similar to  the word saliency scores which can quickly help the model identify key concepts and intents in a spoken question.", "What are some ways to calculate word saliency scores?"], "911": ["After the intents of the spoken question have been established, the model must be able to retrieve facts from a knowledge base, which typically is a graph.", "Therefore, the natural language model needs to take a graph as input."]}, "idea_from": [256935, 120029], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nMy goal is to build a conversational chatbot that can generate full-length natural language answers to spoken questions. Because conversations happen in real time, In order to quickly detect the intents in the spoken questions, I do not want to use large scale neural language models. Instead, I want to utilize heuristics similar to  the word saliency scores which can quickly help the model identify key concepts and intents in a spoken question. What are some ways to calculate word saliency scores? After the intents of the spoken question have been established, the model must be able to retrieve facts from a knowledge base, which typically is a graph. Therefore, the natural language model needs to take a graph as input."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"114": ["115", "217", "223"], "601": ["602", "621"], "682": ["1396", "1397"], "1465": ["217", "695", "539", "130"]}, "sentence_to_aspect_id": {"I am a user marketing manager, and I am planning to train a deep learning model to analyze user engagement data and thus identify key factors that influence it.": ["114"], "With such a model, I can continuously analyze the data and adjust marketing strategies, recommendations and rewards programs to maintain user engagement or increase future revenue.": ["601"], "Also, results from the model can also allow me to treat users with personalized marketing campaigns.": ["682"], "Therefore, I hope the model can track and analyze the data from customers in real-time.": ["1465"]}, "aspect_id_to_sentence": {"114": ["I am a user marketing manager, and I am planning to train a deep learning model to analyze user engagement data and thus identify key factors that influence it."], "601": ["With such a model, I can continuously analyze the data and adjust marketing strategies, recommendations and rewards programs to maintain user engagement or increase future revenue."], "682": ["Also, results from the model can also allow me to treat users with personalized marketing campaigns."], "1465": ["Therefore, I hope the model can track and analyze the data from customers in real-time."]}, "idea_from": [129982], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am a user marketing manager, and I am planning to train a deep learning model to analyze user engagement data and thus identify key factors that influence it. With such a model, I can continuously analyze the data and adjust marketing strategies, recommendations and rewards programs to maintain user engagement or increase future revenue. Also, results from the model can also allow me to treat users with personalized marketing campaigns.  Therefore, I hope the model can track and analyze the data from customers in real-time."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1103": ["1104", "1186", "840", "966"], "1221": ["1104", "1186", "1260"], "817": ["1104", "875", "823"], "821": ["1186", "865"], "1199": ["1104", "1186", "1100", "831"]}, "sentence_to_aspect_id": {"I am seeking a generative modeling approach capable of creating new levels and potentially game settings/environments for a video game with multiple existing levels of difficulty.": ["1103"], "Specifically, I am interested in exploring how Generative Adversarial Networks (GANs) and other generative methods could generate entirely new levels by emulating the style of previous ones.": ["1221"], "It is crucial that the newly generated levels are not merely derivative and that my generative model can optimize specific properties, such as the intensity or graphic nature of the game.": ["817"], "Given that these properties are non-differentiable, I need a method to either render them differentiable or employ a reinforcement learning-centric approach to optimize these rewards.": ["817"], "After generating a variety of levels, I require a method to select some of the best ones.": ["821"], "One potential solution could be to evaluate the generated levels using an automatic metric, such as the performance of an AI agent playing the level.": ["821"], "Alternatively, I am considering designing a derivative-free stochastic optimization algorithm to guide the search across the space of all synthetically generated levels, steering towards those that meet specific objectives.": ["1199"]}, "aspect_id_to_sentence": {"1103": ["I am seeking a generative modeling approach capable of creating new levels and potentially game settings/environments for a video game with multiple existing levels of difficulty."], "1221": ["Specifically, I am interested in exploring how Generative Adversarial Networks (GANs) and other generative methods could generate entirely new levels by emulating the style of previous ones."], "817": ["It is crucial that the newly generated levels are not merely derivative and that my generative model can optimize specific properties, such as the intensity or graphic nature of the game.", "Given that these properties are non-differentiable, I need a method to either render them differentiable or employ a reinforcement learning-centric approach to optimize these rewards."], "821": ["After generating a variety of levels, I require a method to select some of the best ones.", "One potential solution could be to evaluate the generated levels using an automatic metric, such as the performance of an AI agent playing the level."], "1199": ["Alternatively, I am considering designing a derivative-free stochastic optimization algorithm to guide the search across the space of all synthetically generated levels, steering towards those that meet specific objectives."]}, "idea_from": [233501], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am seeking a generative modeling approach capable of creating new levels and potentially game settings/environments for a video game with multiple existing levels of difficulty. Specifically, I am interested in exploring how Generative Adversarial Networks (GANs) and other generative methods could generate entirely new levels by emulating the style of previous ones. It is crucial that the newly generated levels are not merely derivative and that my generative model can optimize specific properties, such as the intensity or graphic nature of the game. Given that these properties are non-differentiable, I need a method to either render them differentiable or employ a reinforcement learning-centric approach to optimize these rewards. After generating a variety of levels, I require a method to select some of the best ones. One potential solution could be to evaluate the generated levels using an automatic metric, such as the performance of an AI agent playing the level. Alternatively, I am considering designing a derivative-free stochastic optimization algorithm to guide the search across the space of all synthetically generated levels, steering towards those that meet specific objectives."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"786": ["785", "784", "1237"], "1289": ["1290", "1307", "1197"], "890": ["1196", "1195"], "1308": ["1290", "1309", "1382"], "757": [], "895": [], "896": []}, "sentence_to_aspect_id": {"I work in the field of medical text mining and I require a system that can accurately extract relevant information from medical documents.": ["786"], "First of all, the system needs a named entity recognition module that is capable of recognizing biomedical entities.": ["1289"], "I am currently thinking about using deep learning based approaches for the named entity recognition module, so I can achieve optimal performance.": ["1289"], "However, since human annotations are expensive and difficult to obtain, I want to explore options where my deep learning module does not need much labeled data, instead, it could train on unlabeled data as well.": ["890"], "Since in real-life, there is a large volume of medical documents, I want to reduce the amount of training time.": ["1308"], "Currently, I think I could use some weight sharing or weight transfer to reduce training time.": ["1308"], "My final goal is to beat current state of the art models as well as pre-trained models.": ["757"], "Overall, efficiency and low-cost are two important factors in my data collection, model training and inference pipeline.": ["895", "896"]}, "aspect_id_to_sentence": {"786": ["I work in the field of medical text mining and I require a system that can accurately extract relevant information from medical documents."], "1289": ["First of all, the system needs a named entity recognition module that is capable of recognizing biomedical entities.", "I am currently thinking about using deep learning based approaches for the named entity recognition module, so I can achieve optimal performance."], "890": ["However, since human annotations are expensive and difficult to obtain, I want to explore options where my deep learning module does not need much labeled data, instead, it could train on unlabeled data as well."], "1308": ["Since in real-life, there is a large volume of medical documents, I want to reduce the amount of training time.", "Currently, I think I could use some weight sharing or weight transfer to reduce training time."], "757": ["My final goal is to beat current state of the art models as well as pre-trained models."], "895": ["Overall, efficiency and low-cost are two important factors in my data collection, model training and inference pipeline."], "896": ["Overall, efficiency and low-cost are two important factors in my data collection, model training and inference pipeline."]}, "idea_from": [249726], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI work in the field of medical text mining and I require a system that can accurately extract relevant information from medical documents. First of all, the system needs a named entity recognition module that is capable of recognizing biomedical entities. I am currently thinking about using deep learning based approaches for the named entity recognition module, so I can achieve optimal performance. However, since human annotations are expensive and difficult to obtain, I want to explore options where my deep learning module does not need much labeled data, instead, it could train on unlabeled data as well.  Since in real-life, there is a large volume of medical documents, I want to reduce the amount of training time. Currently, I think I could use some weight sharing or weight transfer to reduce training time. My final goal is to beat current state of the art models as well as pre-trained models. Overall, efficiency and low-cost are two important factors in my data collection, model training and inference pipeline."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1073": ["1164", "1340"], "1261": ["1164", "989", "836", "972"], "1054": ["1053", "1266"], "1133": [], "1075": ["1164", "715", "716"]}, "sentence_to_aspect_id": {"I am working on a natural language processing project focused on entity recognition, which necessitates the comprehension and processing of vast amounts of text data.": ["1073"], "My goal is to integrate real-world knowledge during the training phase to bolster the model's performance across various tasks, thus bridging the gap between raw text and meaningful entities.": ["1261"], "Therefore, I am in search of models that can generate and learn embeddings for words and entities by leveraging extensive real-world knowledge bases.": ["1054"], "Naturally, the model needs to be able to process knowledge and concept in some differentiable way.": ["1133"], "Ideally, the trained model should excel in performance on entity-centric datasets and produce competitive results on other standard benchmark datasets.": ["1075"]}, "aspect_id_to_sentence": {"1073": ["I am working on a natural language processing project focused on entity recognition, which necessitates the comprehension and processing of vast amounts of text data."], "1261": ["My goal is to integrate real-world knowledge during the training phase to bolster the model's performance across various tasks, thus bridging the gap between raw text and meaningful entities."], "1054": ["Therefore, I am in search of models that can generate and learn embeddings for words and entities by leveraging extensive real-world knowledge bases."], "1133": ["Naturally, the model needs to be able to process knowledge and concept in some differentiable way."], "1075": ["Ideally, the trained model should excel in performance on entity-centric datasets and produce competitive results on other standard benchmark datasets."]}, "idea_from": [205857], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am working on a natural language processing project focused on entity recognition, which necessitates the comprehension and processing of vast amounts of text data. My goal is to integrate real-world knowledge during the training phase to bolster the model's performance across various tasks, thus bridging the gap between raw text and meaningful entities. Therefore, I am in search of models that can generate and learn embeddings for words and entities by leveraging extensive real-world knowledge bases. Naturally, the model needs to be able to process knowledge and concept in some differentiable way. Ideally, the trained model should excel in performance on entity-centric datasets and produce competitive results on other standard benchmark datasets."}


I am currently working on a project that involves recognizing text in images, utilizing the attention-based encoder-decoder framework. However, I am encountering difficulties when the system misinterprets words with extra or missing characters in the text, which complicates the training process and reduces accuracy. I am in search of a model capable of assigning probabilities to words within the images, specifically regarding their likelihood of containing missing or superfluous characters. Additionally, I require techniques that can be integrated into the existing framework. Ideally, these techniques would enable the framework to identify potential spelling errors and attempt to restore the original, correct word. I anticipate that the implementation of such a model and techniques will enhance the performance of my text recognition framework. To test this, I am also seeking datasets that contain spelling errors. I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models’ performance in a reliable way.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"400": ["418", "332"], "213": ["332", "218", "692"], "282": ["332", "370", "456"], "385": [], "440": [], "285": []}, "sentence_to_aspect_id": {"I am currently working on a project that involves recognizing text in images, utilizing the attention-based encoder-decoder framework.": ["400"], "However, I am encountering difficulties when the system misinterprets words with extra or missing characters in the text, which complicates the training process and reduces accuracy.": [], "I am in search of a model capable of assigning probabilities to words within the images, specifically regarding their likelihood of containing missing or superfluous characters.": ["213"], "Additionally, I require techniques that can be integrated into the existing framework.": ["282"], "Ideally, these techniques would enable the framework to identify potential spelling errors and attempt to restore the original, correct word.": ["282"], "I anticipate that the implementation of such a model and techniques will enhance the performance of my text recognition framework.": ["282"], "To test this, I am also seeking datasets that contain spelling errors.": ["385"], "I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models\u2019 performance in a reliable way.": ["440", "285"]}, "aspect_id_to_sentence": {"400": ["I am currently working on a project that involves recognizing text in images, utilizing the attention-based encoder-decoder framework."], "213": ["I am in search of a model capable of assigning probabilities to words within the images, specifically regarding their likelihood of containing missing or superfluous characters."], "282": ["Additionally, I require techniques that can be integrated into the existing framework.", "Ideally, these techniques would enable the framework to identify potential spelling errors and attempt to restore the original, correct word.", "I anticipate that the implementation of such a model and techniques will enhance the performance of my text recognition framework."], "385": ["To test this, I am also seeking datasets that contain spelling errors."], "440": ["I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models\u2019 performance in a reliable way."], "285": ["I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models\u2019 performance in a reliable way."]}, "idea_from": [232706], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am currently working on a project that involves recognizing text in images, utilizing the attention-based encoder-decoder framework. However, I am encountering difficulties when the system misinterprets words with extra or missing characters in the text, which complicates the training process and reduces accuracy. I am in search of a model capable of assigning probabilities to words within the images, specifically regarding their likelihood of containing missing or superfluous characters. Additionally, I require techniques that can be integrated into the existing framework. Ideally, these techniques would enable the framework to identify potential spelling errors and attempt to restore the original, correct word. I anticipate that the implementation of such a model and techniques will enhance the performance of my text recognition framework. To test this, I am also seeking datasets that contain spelling errors. I would appreciate any guidance on how to effectively train the model with these datasets and how to evaluate the models\u2019 performance in a reliable way."}


Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"358": ["422", "405"], "1419": ["1410", "1411"], "769": ["422", "1436", "1429"], "482": ["429", "262"], "768": ["422", "1434", "1435"], "770": ["422", "1426", "1444"], "772": ["422", "1443", "427"], "771": []}, "sentence_to_aspect_id": {"I want to introduce an operation planning system for transportation authorities and truck companies.": ["358"], "This system will coordinate between different truck companies and transportation authorities to make a driving plan for each truck, for example including when it should move, when it should stop and refuel, and in what route it should be on.": ["1419"], "This system will optimize the safety for truck and truck drivers because it will identify highway sections that are crowded.": ["769"], "Therefore, currently I am thinking about using data mining approaches to learn patterns in truck routes.": ["482"], "These approaches could also identify sets of trucks that tend to move together and where they are headed.": ["768"], "I am planning to use spatial temporal data about truck movements from real-life truck fleet planning systems in Liaoning, China, which is readily available.": ["770"], "From this dataset, I hope to  determine the feasibility of coordinating spontaneous truck platoons through speed adjustment.": ["772"], "Overall, the system should also reduce total fuel consumption to achieve the goal of energy savings.": ["771"]}, "aspect_id_to_sentence": {"358": ["I want to introduce an operation planning system for transportation authorities and truck companies."], "1419": ["This system will coordinate between different truck companies and transportation authorities to make a driving plan for each truck, for example including when it should move, when it should stop and refuel, and in what route it should be on."], "769": ["This system will optimize the safety for truck and truck drivers because it will identify highway sections that are crowded."], "482": ["Therefore, currently I am thinking about using data mining approaches to learn patterns in truck routes."], "768": ["These approaches could also identify sets of trucks that tend to move together and where they are headed."], "770": ["I am planning to use spatial temporal data about truck movements from real-life truck fleet planning systems in Liaoning, China, which is readily available."], "772": ["From this dataset, I hope to  determine the feasibility of coordinating spontaneous truck platoons through speed adjustment."], "771": ["Overall, the system should also reduce total fuel consumption to achieve the goal of energy savings."]}, "idea_from": [95172], "num_aspects": 8, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want to introduce an operation planning system for transportation authorities and truck companies.  This system will coordinate between different truck companies and transportation authorities to make a driving plan for each truck, for example including when it should move, when it should stop and refuel, and in what route it should be on. This system will optimize the safety for truck and truck drivers because it will identify highway sections that are crowded. Therefore, currently I am thinking about using data mining approaches to learn patterns in truck routes. These approaches could also identify sets of trucks that tend to move together and where they are headed. I am planning to use spatial temporal data about truck movements from real-life truck fleet planning systems in Liaoning, China, which is readily available. From this dataset, I hope to  determine the feasibility of coordinating spontaneous truck platoons through speed adjustment. Overall, the system should also reduce total fuel consumption to achieve the goal of energy savings."}


My cousin is struggling with math and I want to help him. I need a system that can help people to understand arithmetic operations and how to solve algebraic equations. The system needs to be able to generate answer rationales that can include detailed steps taken to arrive at a final answer. It would be best if the system can provide a framework for the structure of the answer that helps my cousin to understand the standard way of solving math problems. Also, I hope the system can adapt its answer rationales to different levels of understanding and provide feedback to the user’s progress. I hope with the help of the system, my cousin can not only understand math but also love math.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1147": [], "1145": ["1144", "22"], "1148": [], "1140": ["1146", "1141", "1149"]}, "sentence_to_aspect_id": {"My cousin is struggling with math and I want to help him.": ["1147"], "I need a system that can help people to understand arithmetic operations and how to solve algebraic equations.": ["1147"], "The system needs to be able to generate answer rationales that can include detailed steps taken to arrive at a final answer.": ["1145"], "It would be best if the system can provide a framework for the structure of the answer that helps my cousin to understand the standard way of solving math problems.": ["1148"], "Also, I hope the system can adapt its answer rationales to different levels of understanding and provide feedback to the user\u2019s progress.": ["1140"], "I hope with the help of the system, my cousin can not only understand math but also love math.": ["1147", "1145", "1148", "1140"]}, "aspect_id_to_sentence": {"1147": ["My cousin is struggling with math and I want to help him.", "I need a system that can help people to understand arithmetic operations and how to solve algebraic equations.", "I hope with the help of the system, my cousin can not only understand math but also love math."], "1145": ["The system needs to be able to generate answer rationales that can include detailed steps taken to arrive at a final answer.", "I hope with the help of the system, my cousin can not only understand math but also love math."], "1148": ["It would be best if the system can provide a framework for the structure of the answer that helps my cousin to understand the standard way of solving math problems.", "I hope with the help of the system, my cousin can not only understand math but also love math."], "1140": ["Also, I hope the system can adapt its answer rationales to different levels of understanding and provide feedback to the user\u2019s progress.", "I hope with the help of the system, my cousin can not only understand math but also love math."]}, "idea_from": [266702], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nMy cousin is struggling with math and I want to help him. I need a system that can help people to understand arithmetic operations and how to solve algebraic equations. The system needs to be able to generate answer rationales that can include detailed steps taken to arrive at a final answer. It would be best if the system can provide a framework for the structure of the answer that helps my cousin to understand the standard way of solving math problems. Also, I hope the system can adapt its answer rationales to different levels of understanding and provide feedback to the user\u2019s progress. I hope with the help of the system, my cousin can not only understand math but also love math."}


I want to make an analysis about the users’ behavior in a social media platform. With such analysis, I can find more effective marketing strategies. Since the platform allows the users to use informal and abbreviated language like cuz or LOL, I need a model that can identify informal language, slang and abbreviated words and match them with their formal explanation, so that I can make a further analysis based on the pre-processed data. I am looking for text mining techniques that can be used in the model. Also, the accuracy of the identification and interpretation of informal languages is crucial, and it would be best if the model can extract users’ intention from the text. I am looking for multiple algorithms that can be used in such models so that I can compare the effectiveness and evaluate their accuracy in my dataset.

Instruction: Given a query, find relevant scientific papers which satisfy the query

{"aspects": {"1166": ["1298", "1466"], "1185": ["1298", "1466", "7"], "725": ["726", "727", "728"], "1334": [], "1238": []}, "sentence_to_aspect_id": {"I want to make an analysis about the users\u2019 behavior in a social media platform.": ["1166"], "With such analysis, I can find more effective marketing strategies.": ["1185"], "Since the platform allows the users to use informal and abbreviated language like cuz or LOL, I need a model that can identify informal language, slang and abbreviated words and match them with their formal explanation, so that I can make a further analysis based on the pre-processed data.": ["725"], "I am looking for text mining techniques that can be used in the model.": ["1334"], "Also, the accuracy of the identification and interpretation of informal languages is crucial, and it would be best if the model can extract users\u2019 intention from the text.": ["1334", "1238"], "I am looking for multiple algorithms that can be used in such models so that I can compare the effectiveness and evaluate their accuracy in my dataset.": ["1185", "1334"]}, "aspect_id_to_sentence": {"1166": ["I want to make an analysis about the users\u2019 behavior in a social media platform."], "1185": ["With such analysis, I can find more effective marketing strategies.", "I am looking for multiple algorithms that can be used in such models so that I can compare the effectiveness and evaluate their accuracy in my dataset."], "725": ["Since the platform allows the users to use informal and abbreviated language like cuz or LOL, I need a model that can identify informal language, slang and abbreviated words and match them with their formal explanation, so that I can make a further analysis based on the pre-processed data."], "1334": ["I am looking for text mining techniques that can be used in the model.", "Also, the accuracy of the identification and interpretation of informal languages is crucial, and it would be best if the model can extract users\u2019 intention from the text.", "I am looking for multiple algorithms that can be used in such models so that I can compare the effectiveness and evaluate their accuracy in my dataset."], "1238": ["Also, the accuracy of the identification and interpretation of informal languages is crucial, and it would be best if the model can extract users\u2019 intention from the text."]}, "idea_from": [146082], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want to make an analysis about the users\u2019 behavior in a social media platform. With such analysis, I can find more effective marketing strategies. Since the platform allows the users to use informal and abbreviated language like cuz or LOL, I need a model that can identify informal language, slang and abbreviated words and match them with their formal explanation, so that I can make a further analysis based on the pre-processed data. I am looking for text mining techniques that can be used in the model. Also, the accuracy of the identification and interpretation of informal languages is crucial, and it would be best if the model can extract users\u2019 intention from the text. I am looking for multiple algorithms that can be used in such models so that I can compare the effectiveness and evaluate their accuracy in my dataset."}