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

{"aspects": {"1066": ["1296", "1117", "803"], "1067": ["1296", "1287"], "858": ["1315", "993", "1349"], "1385": []}, "sentence_to_aspect_id": {"As a linguist, I aim to efficiently categorize various semantic elements within a sentence.": [], "For instance, I want to pinpoint the predicate-argument structure.": [], "I plan to create a model based on neural networks that enhances traditional methods which utilize syntactic trees.": ["1066"], "Rather than directly employing syntactic trees, my model will learn to convert syntax and grammatical structure into vectorized representations.": ["1067"], "These hidden representations will allow my model to be differentiable and converge rapidly.": ["858"], "I'm also curious to see if using a semantic representation of my text from a large pre-trained language model could boost the accuracy of my labeling.": ["858"], "Ultimately, I intend to test my model against a wide range of linguistic benchmarks.": ["1385"]}, "aspect_id_to_sentence": {"1066": ["I plan to create a model based on neural networks that enhances traditional methods which utilize syntactic trees."], "1067": ["Rather than directly employing syntactic trees, my model will learn to convert syntax and grammatical structure into vectorized representations."], "858": ["These hidden representations will allow my model to be differentiable and converge rapidly.", "I'm also curious to see if using a semantic representation of my text from a large pre-trained language model could boost the accuracy of my labeling."], "1385": ["Ultimately, I intend to test my model against a wide range of linguistic benchmarks."]}, "idea_from": [156847], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nAs a linguist, I aim to efficiently categorize various semantic elements within a sentence. For instance, I want to pinpoint the predicate-argument structure. I plan to create a model based on neural networks that enhances traditional methods which utilize syntactic trees. Rather than directly employing syntactic trees, my model will learn to convert syntax and grammatical structure into vectorized representations. These hidden representations will allow my model to be differentiable and converge rapidly. I'm also curious to see if using a semantic representation of my text from a large pre-trained language model could boost the accuracy of my labeling. Ultimately, I intend to test my model against a wide range of linguistic benchmarks."}


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

{"aspects": {"120": ["119", "145"], "399": ["119", "149", "615"], "25": ["119", "194", "302"], "377": ["119", "702", "704", "178"], "291": ["119", "178", "597"], "671": ["119", "620", "10"]}, "sentence_to_aspect_id": {"I am working for a social media platform and my task is to automatically generate captions for user-uploaded images so that overall user experience is enhanced.": ["120"], "I know that computer vision models can effectively do image captioning but for similar images they tend to give out the same vanilla and bland descriptions.": ["120"], "I need to be building a model that could introduce diversity into the process of image captioning.": ["399"], "I want my model to be able to  generate distinct captions for similar images.": ["399"], "Some ideas that I have so far include: using a customized weighted loss function so that the model will be penalized for bland descriptions.": ["25"], "Secondly, use reinforcement learning and treat diversity as reward in the training process.": ["377"], "Thirdly, I could also group similar images together as a training bundle.": ["291"], "I also need some new metrics to evaluate the distinctiveness of the captions.": ["671"], "Finally, I would like my model to outperform state-of-the-art image captioning models in terms of diversity with minimal degradation in accuracy.": ["671"]}, "aspect_id_to_sentence": {"120": ["I am working for a social media platform and my task is to automatically generate captions for user-uploaded images so that overall user experience is enhanced.", "I know that computer vision models can effectively do image captioning but for similar images they tend to give out the same vanilla and bland descriptions."], "399": ["I need to be building a model that could introduce diversity into the process of image captioning.", "I want my model to be able to  generate distinct captions for similar images."], "25": ["Some ideas that I have so far include: using a customized weighted loss function so that the model will be penalized for bland descriptions."], "377": ["Secondly, use reinforcement learning and treat diversity as reward in the training process."], "291": ["Thirdly, I could also group similar images together as a training bundle."], "671": ["I also need some new metrics to evaluate the distinctiveness of the captions.", "Finally, I would like my model to outperform state-of-the-art image captioning models in terms of diversity with minimal degradation in accuracy."]}, "idea_from": [112250], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am working for a social media platform and my task is to automatically generate captions for user-uploaded images so that overall user experience is enhanced. I know that computer vision models can effectively do image captioning but for similar images they tend to give out the same vanilla and bland descriptions. I need to be building a model that could introduce diversity into the process of image captioning. I want my model to be able to  generate distinct captions for similar images. Some ideas that I have so far include: using a customized weighted loss function so that the model will be penalized for bland descriptions. Secondly, use reinforcement learning and treat diversity as reward in the training process. Thirdly, I could also group similar images together as a training bundle.  I also need some new metrics to evaluate the distinctiveness of the captions. Finally, I would like my model to outperform state-of-the-art image captioning models in terms of diversity with minimal degradation in accuracy. "}


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

{"aspects": {"1105": ["1265", "822"], "1040": ["1265", "1129", "846"], "1039": ["1265", "1059", "1346"], "1336": ["1314", "1273"], "937": ["971", "843"], "820": [], "1202": ["1265", "1342", "979"]}, "sentence_to_aspect_id": {"My goal is to develop a learning model that can handle multiple tasks simultaneously.": ["1105"], "This proposed learning model will function as a sub-model selector, meaning that when presented with a new task, it will determine the most suitable sub-model to learn this task.": ["1040"], "Additionally, my learning model could also operate as a sub-model constructor.": ["1039"], "If it determines that no existing sub-model can learn the task, it will generate innovative model architectures to construct a new sub-model suitable for the task.": ["1039"], "At present, I am considering the application of a combination of reinforcement learning and model architecture search algorithms to achieve this.": ["1336"], "To effectively evaluate the performance, I require a comprehensive benchmark that includes a variety of datasets that can be used as different sub-tasks.": ["937"], "My aim is for my learning model to achieve state-of-the-art performance on this benchmark.": ["820"], "In general, I am open to any methodologies that enhance the performance and adaptability of my learning model across different problem domains.": ["1202"]}, "aspect_id_to_sentence": {"1105": ["My goal is to develop a learning model that can handle multiple tasks simultaneously."], "1040": ["This proposed learning model will function as a sub-model selector, meaning that when presented with a new task, it will determine the most suitable sub-model to learn this task."], "1039": ["Additionally, my learning model could also operate as a sub-model constructor.", "If it determines that no existing sub-model can learn the task, it will generate innovative model architectures to construct a new sub-model suitable for the task."], "1336": ["At present, I am considering the application of a combination of reinforcement learning and model architecture search algorithms to achieve this."], "937": ["To effectively evaluate the performance, I require a comprehensive benchmark that includes a variety of datasets that can be used as different sub-tasks."], "820": ["My aim is for my learning model to achieve state-of-the-art performance on this benchmark."], "1202": ["In general, I am open to any methodologies that enhance the performance and adaptability of my learning model across different problem domains."]}, "idea_from": [231489], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nMy goal is to develop a learning model that can handle multiple tasks simultaneously. This proposed learning model will function as a sub-model selector, meaning that when presented with a new task, it will determine the most suitable sub-model to learn this task. Additionally, my learning model could also operate as a sub-model constructor. If it determines that no existing sub-model can learn the task, it will generate innovative model architectures to construct a new sub-model suitable for the task. At present, I am considering the application of a combination of reinforcement learning and model architecture search algorithms to achieve this. To effectively evaluate the performance, I require a comprehensive benchmark that includes a variety of datasets that can be used as different sub-tasks. My aim is for my learning model to achieve state-of-the-art performance on this benchmark. In general, I am open to any methodologies that enhance the performance and adaptability of my learning model across different problem domains."}


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

{"aspects": {"1175": ["1176", "874"], "893": ["891", "991"], "815": ["1176", "816"], "888": ["1176", "887", "856"], "1038": ["1176", "802"]}, "sentence_to_aspect_id": {"I am currently developing a chatbot capable of engaging in open-ended conversations with users.": ["1175"], "However, I am dissatisfied with the conventional temperature-based stochastic sampling decoding procedure, as it results in derivative output that lacks creativity and diversity.": ["893"], "I am seeking literature that explores the issue of diversity deficiency in language model decoding procedures.": ["893"], "Additionally, I aim to create a decoding scheme that strikes a balance between output diversity and quality.": ["815"], "I am also interested in research that examines the phenomenon of high-likelihood sentences exhibiting unexpectedly low quality.": ["888"], "I wish to understand this occurrence and prevent it from affecting my decoding scheme.": ["888"], "Ultimately, my goal is to develop a model that surpasses existing decoding algorithms in terms of balancing the quality and diversity of generated responses.": ["1038"]}, "aspect_id_to_sentence": {"1175": ["I am currently developing a chatbot capable of engaging in open-ended conversations with users."], "893": ["However, I am dissatisfied with the conventional temperature-based stochastic sampling decoding procedure, as it results in derivative output that lacks creativity and diversity.", "I am seeking literature that explores the issue of diversity deficiency in language model decoding procedures."], "815": ["Additionally, I aim to create a decoding scheme that strikes a balance between output diversity and quality."], "888": ["I am also interested in research that examines the phenomenon of high-likelihood sentences exhibiting unexpectedly low quality.", "I wish to understand this occurrence and prevent it from affecting my decoding scheme."], "1038": ["Ultimately, my goal is to develop a model that surpasses existing decoding algorithms in terms of balancing the quality and diversity of generated responses."]}, "idea_from": [129680], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am currently developing a chatbot capable of engaging in open-ended conversations with users. However, I am dissatisfied with the conventional temperature-based stochastic sampling decoding procedure, as it results in derivative output that lacks creativity and diversity. I am seeking literature that explores the issue of diversity deficiency in language model decoding procedures. Additionally, I aim to create a decoding scheme that strikes a balance between output diversity and quality. I am also interested in research that examines the phenomenon of high-likelihood sentences exhibiting unexpectedly low quality. I wish to understand this occurrence and prevent it from affecting my decoding scheme. Ultimately, my goal is to develop a model that surpasses existing decoding algorithms in terms of balancing the quality and diversity of generated responses."}


I need to simplify legal documents for individuals with limited English proficiency. To do this, I plan to train a lexical simplification model that can identify complex words in a given sentence and replace them with simpler alternatives while maintaining the sentence’s meaning. I want to find a model that takes a complex word and its context as input and generates reasonable candidate substitutions that do not change the meaning of the sentence. I also want to find state-of-the-art models that can predict the masked words from its context as a reference of my model when generating candidate substitutions. For all these models, I want both supervised and unsupervised ones. By comparing the performance of these models, I can decide whether my model should be a supervised one or not.

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

{"aspects": {"1249": ["1200"], "1106": ["1352", "1120", "1125"], "913": [], "909": [], "1326": ["1327", "1374"]}, "sentence_to_aspect_id": {"I need to simplify legal documents for individuals with limited English proficiency.": ["1249"], "To do this, I plan to train a lexical simplification model that can identify complex words in a given sentence and replace them with simpler alternatives while maintaining the sentence\u2019s meaning.": ["1106"], "I want to find a model that takes a complex word and its context as input and generates reasonable candidate substitutions that do not change the meaning of the sentence.": ["913"], "I also want to find state-of-the-art models that can predict the masked words from its context as a reference of my model when generating candidate substitutions.": ["909"], "For all these models, I want both supervised and unsupervised ones.": ["1326"], "By comparing the performance of these models, I can decide whether my model should be a supervised one or not.": ["1326"]}, "aspect_id_to_sentence": {"1249": ["I need to simplify legal documents for individuals with limited English proficiency."], "1106": ["To do this, I plan to train a lexical simplification model that can identify complex words in a given sentence and replace them with simpler alternatives while maintaining the sentence\u2019s meaning."], "913": ["I want to find a model that takes a complex word and its context as input and generates reasonable candidate substitutions that do not change the meaning of the sentence."], "909": ["I also want to find state-of-the-art models that can predict the masked words from its context as a reference of my model when generating candidate substitutions."], "1326": ["For all these models, I want both supervised and unsupervised ones.", "By comparing the performance of these models, I can decide whether my model should be a supervised one or not."]}, "idea_from": [176154], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI need to simplify legal documents for individuals with limited English proficiency. To do this, I plan to train a lexical simplification model that can identify complex words in a given sentence and replace them with simpler alternatives while maintaining the sentence\u2019s meaning. I want to find a model that takes a complex word and its context as input and generates reasonable candidate substitutions that do not change the meaning of the sentence. I also want to find state-of-the-art models that can predict the masked words from its context as a reference of my model when generating candidate substitutions. For all these models, I want both supervised and unsupervised ones. By comparing the performance of these models, I can decide whether my model should be a supervised one or not."}


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

{"aspects": {"1339": [], "1013": [], "898": [], "1032": [], "1222": [], "1223": [], "798": ["40", "999", "799", "894"]}, "sentence_to_aspect_id": {"I need a literature review of the clinician system for a project I am planning to do.": ["1339"], "I am specifically interested in the application of reinforcement learning (RL) in the clinician system and identify the benefits of RL for the healthcare research community.": ["1013", "898"], "I hope the literature review can contain detailed exploration of the findings from the literature, including an explanation of the clinician system, how intensive care data can be utilized, and how reinforcement learning can help to make treatment recommendations.": ["1032", "1222", "1223"], "I hope the literature review also compares the differences of different RL algorithms used in the clinician system field, and finds out the one that is most efficient under large amounts of data.": ["798"]}, "aspect_id_to_sentence": {"1339": ["I need a literature review of the clinician system for a project I am planning to do."], "1013": ["I am specifically interested in the application of reinforcement learning (RL) in the clinician system and identify the benefits of RL for the healthcare research community."], "898": ["I am specifically interested in the application of reinforcement learning (RL) in the clinician system and identify the benefits of RL for the healthcare research community."], "1032": ["I hope the literature review can contain detailed exploration of the findings from the literature, including an explanation of the clinician system, how intensive care data can be utilized, and how reinforcement learning can help to make treatment recommendations."], "1222": ["I hope the literature review can contain detailed exploration of the findings from the literature, including an explanation of the clinician system, how intensive care data can be utilized, and how reinforcement learning can help to make treatment recommendations."], "1223": ["I hope the literature review can contain detailed exploration of the findings from the literature, including an explanation of the clinician system, how intensive care data can be utilized, and how reinforcement learning can help to make treatment recommendations."], "798": ["I hope the literature review also compares the differences of different RL algorithms used in the clinician system field, and finds out the one that is most efficient under large amounts of data."]}, "idea_from": [196267], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI need a literature review of the clinician system for a project I am planning to do. I am specifically interested in the application of reinforcement learning (RL) in the clinician system and identify the benefits of RL for the healthcare research community. I hope the literature review can contain detailed exploration of the findings from the literature, including an explanation of the clinician system, how intensive care data can be utilized, and how reinforcement learning can help to make treatment recommendations. I hope the literature review also compares the differences of different RL algorithms used in the clinician system field, and finds out the one that is most efficient under large amounts of data."}


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

{"aspects": {"1313": [], "1015": [], "1168": [], "1283": [], "35": [], "1061": ["1062", "1060", "1049", "1058"], "1134": ["1135", "1094"]}, "sentence_to_aspect_id": {"As a professional game player, I am interested in reinforcement learning algorithms.": ["1313"], "However, some of the algorithms simply start training without a good initialization, and I am looking for some different algorithms.": ["1015"], "I heard there is a technique that is called behavioral cloning, which allows the data to learn from good players initially.": ["1168"], "The model should be able to identify and record data from humans accurately and minimize the impact of human reflexes.": ["1283"], "I have tons of videos of myself playing dozens of games that I am quite familiar with and good at.": ["35"], "These videos may work as good initialization.": ["35"], "Then, I hope the model can use traditional reinforcement learning strategies to send keystrokes to a game, analyze game mechanics, and make better decisions.": ["1061"], "Since I can provide a good initialization, I hope the model does not need to modify the game and can achieve better performance at the same time when compared to other models that are initialized randomly.": ["1134"], "I hope such models can provide inspiration to me to play the game better in the future.": []}, "aspect_id_to_sentence": {"1313": ["As a professional game player, I am interested in reinforcement learning algorithms."], "1015": ["However, some of the algorithms simply start training without a good initialization, and I am looking for some different algorithms."], "1168": ["I heard there is a technique that is called behavioral cloning, which allows the data to learn from good players initially."], "1283": ["The model should be able to identify and record data from humans accurately and minimize the impact of human reflexes."], "35": ["I have tons of videos of myself playing dozens of games that I am quite familiar with and good at.", "These videos may work as good initialization."], "1061": ["Then, I hope the model can use traditional reinforcement learning strategies to send keystrokes to a game, analyze game mechanics, and make better decisions."], "1134": ["Since I can provide a good initialization, I hope the model does not need to modify the game and can achieve better performance at the same time when compared to other models that are initialized randomly."]}, "idea_from": [133479], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nAs a professional game player, I am interested in reinforcement learning algorithms. However, some of the algorithms simply start training without a good initialization, and I am looking for some different algorithms. I heard there is a technique that is called behavioral cloning, which allows the data to learn from good players initially. The model should be able to identify and record data from humans accurately and minimize the impact of human reflexes. I have tons of videos of myself playing dozens of games that I am quite familiar with and good at. These videos may work as good initialization. Then, I hope the model can use traditional reinforcement learning strategies to send keystrokes to a game, analyze game mechanics, and make better decisions. Since I can provide a good initialization, I hope the model does not need to modify the game and can achieve better performance at the same time when compared to other models that are initialized randomly. I hope such models can provide inspiration to me to play the game better in the future."}


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

{"aspects": {"329": ["321", "328"], "759": ["328", "749"], "760": ["328", "710", "711"], "8": ["321", "97", "643", "616"]}, "sentence_to_aspect_id": {"In archaeology, a lot of old photos are produced by polarization cameras, and I am trying to reconstruct an accurate 3D model of an object using images from these polarization cameras.": ["329"], "However, I have difficulties determining the surface of the object because of the features of the polarization camera.": ["759"], "I need a model that can analyze the angle of polarization of reflected light in captured images.": ["759"], "I think both geometric and photometric cues extracted from input color polarization images should be utilized to complete the analysis.": ["760"], "I also need a model that can optimize photometric rendering errors and handle different materials given an initial standard geometric reconstruction.": ["8"], "I will test the models on synthetic and real data and use them as an inspiration for my model.": ["8"]}, "aspect_id_to_sentence": {"329": ["In archaeology, a lot of old photos are produced by polarization cameras, and I am trying to reconstruct an accurate 3D model of an object using images from these polarization cameras."], "759": ["However, I have difficulties determining the surface of the object because of the features of the polarization camera.", "I need a model that can analyze the angle of polarization of reflected light in captured images."], "760": ["I need a model that can analyze the angle of polarization of reflected light in captured images.", "I think both geometric and photometric cues extracted from input color polarization images should be utilized to complete the analysis."], "8": ["I also need a model that can optimize photometric rendering errors and handle different materials given an initial standard geometric reconstruction.", "I will test the models on synthetic and real data and use them as an inspiration for my model."]}, "idea_from": [111407], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn archaeology, a lot of old photos are produced by polarization cameras, and I am trying to reconstruct an accurate 3D model of an object using images from these polarization cameras. However, I have difficulties determining the surface of the object because of the features of the polarization camera. I need a model that can analyze the angle of polarization of reflected light in captured images. I think both geometric and photometric cues extracted from input color polarization images should be utilized to complete the analysis. I also need a model that can optimize photometric rendering errors and handle different materials given an initial standard geometric reconstruction. I will test the models on synthetic and real data and use them as an inspiration for my model."}


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

{"aspects": {"1101": ["1102", "852", "988"], "42": ["1162", "829", "830", "1343"], "1370": ["1213", "884", "1209"], "1023": ["1006", "852", "988"]}, "sentence_to_aspect_id": {"When modeling complex probability distributions of variables with inherent dependencies, I utilize a graph to represent these dependencies and probabilistic models to depict the variables' distributions.": ["1101"], "However, these models can be large and computationally expensive during inference.": [], "Therefore, I am seeking a fast, approximate inference algorithm that can access a highly effective proposal distribution for importance sampling.": ["1101"], "My current strategy involves identifying non-essential variables and marginalizing them during sampling to enhance the speed of inference.": ["42"], "Additionally, I am considering transforming the graphical model into a more efficient format, such as a decision diagram, to facilitate easier interpretation during sampling (inference).": ["1370"], "To evaluate the effectiveness of my approach, I need to test it against standard benchmarks.": ["1023"], "Therefore, I would appreciate information on the standard benchmarks applicable to this task.": ["1023"]}, "aspect_id_to_sentence": {"1101": ["When modeling complex probability distributions of variables with inherent dependencies, I utilize a graph to represent these dependencies and probabilistic models to depict the variables' distributions.", "Therefore, I am seeking a fast, approximate inference algorithm that can access a highly effective proposal distribution for importance sampling."], "42": ["My current strategy involves identifying non-essential variables and marginalizing them during sampling to enhance the speed of inference."], "1370": ["Additionally, I am considering transforming the graphical model into a more efficient format, such as a decision diagram, to facilitate easier interpretation during sampling (inference)."], "1023": ["To evaluate the effectiveness of my approach, I need to test it against standard benchmarks.", "Therefore, I would appreciate information on the standard benchmarks applicable to this task."]}, "idea_from": [229738], "num_aspects": 4, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nWhen modeling complex probability distributions of variables with inherent dependencies, I utilize a graph to represent these dependencies and probabilistic models to depict the variables' distributions. However, these models can be large and computationally expensive during inference. Therefore, I am seeking a fast, approximate inference algorithm that can access a highly effective proposal distribution for importance sampling. My current strategy involves identifying non-essential variables and marginalizing them during sampling to enhance the speed of inference. Additionally, I am considering transforming the graphical model into a more efficient format, such as a decision diagram, to facilitate easier interpretation during sampling (inference). To evaluate the effectiveness of my approach, I need to test it against standard benchmarks. Therefore, I would appreciate information on the standard benchmarks applicable to this task."}


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

{"aspects": {"833": ["794", "855"], "1364": ["794", "1379"], "941": ["992", "1000", "1205"], "939": ["1379", "901"], "1384": ["943", "1383", "805"], "774": []}, "sentence_to_aspect_id": {"In my reinforcement learning (RL) research, I encountered a significant challenge: the inability of a policy learned in one environment to adapt or generalize to another.": [], "I am seeking an efficient method to transfer policies between environments without the need to train a new policy from scratch each time the environment changes.": ["833"], "Currently, I am considering the use of natural language to describe the entire dynamics of the environment or to outline the anticipated changes in the environment.": ["1364"], "To implement this, I require a language model capable of encoding these natural language descriptions.": ["941"], "The resulting encoded latent representations should aid in facilitating policy adaptation to different environments.": ["941"], "It appears crucial to align the text's meaning with the environment's dynamics, including transition probability and reward functions.": ["939"], "Ultimately, I aim to assess the effectiveness of this approach in terms of transfer and multi-task scenarios across various environments and compare it with existing models.": ["1384"], "I am interested in any literature related to the use of textual descriptions in RL and any existing datasets suitable for evaluating my proposed methodology.": ["774"]}, "aspect_id_to_sentence": {"833": ["I am seeking an efficient method to transfer policies between environments without the need to train a new policy from scratch each time the environment changes."], "1364": ["Currently, I am considering the use of natural language to describe the entire dynamics of the environment or to outline the anticipated changes in the environment."], "941": ["To implement this, I require a language model capable of encoding these natural language descriptions.", "The resulting encoded latent representations should aid in facilitating policy adaptation to different environments."], "939": ["It appears crucial to align the text's meaning with the environment's dynamics, including transition probability and reward functions."], "1384": ["Ultimately, I aim to assess the effectiveness of this approach in terms of transfer and multi-task scenarios across various environments and compare it with existing models."], "774": ["I am interested in any literature related to the use of textual descriptions in RL and any existing datasets suitable for evaluating my proposed methodology."]}, "idea_from": [259693], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn my reinforcement learning (RL) research, I encountered a significant challenge: the inability of a policy learned in one environment to adapt or generalize to another. I am seeking an efficient method to transfer policies between environments without the need to train a new policy from scratch each time the environment changes. Currently, I am considering the use of natural language to describe the entire dynamics of the environment or to outline the anticipated changes in the environment. To implement this, I require a language model capable of encoding these natural language descriptions. The resulting encoded latent representations should aid in facilitating policy adaptation to different environments. It appears crucial to align the text's meaning with the environment's dynamics, including transition probability and reward functions. Ultimately, I aim to assess the effectiveness of this approach in terms of transfer and multi-task scenarios across various environments and compare it with existing models. I am interested in any literature related to the use of textual descriptions in RL and any existing datasets suitable for evaluating my proposed methodology."}


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

{"aspects": {"351": ["414", "123"], "299": ["300", "593", "592", "641"], "515": ["300", "152"], "433": ["414", "300", "432"], "455": ["300", "499", "152"]}, "sentence_to_aspect_id": {"With the advent of big data, we often face the challenge of complex heterogeneity within a dataset.": ["351"], "In order to keep user privacy, we would try to avoid sending user data directly to the cloud.": ["351"], "Instead, we want a system working on personal devices that can model complex heterogeneity locally.": ["351"], "We need a heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity.": ["299"], "The framework should also include model distillation techniques such as pruning.": ["515"], "Since we need to run the framework on personal devices like smartphones, the framework should provide some guarantee on performance after distillation.": ["433"], "I am looking for strategies using semi-definite programming to estimate the error bound of the distilled framework.": ["455"]}, "aspect_id_to_sentence": {"351": ["With the advent of big data, we often face the challenge of complex heterogeneity within a dataset.", "In order to keep user privacy, we would try to avoid sending user data directly to the cloud.", "Instead, we want a system working on personal devices that can model complex heterogeneity locally."], "299": ["We need a heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity."], "515": ["The framework should also include model distillation techniques such as pruning."], "433": ["Since we need to run the framework on personal devices like smartphones, the framework should provide some guarantee on performance after distillation."], "455": ["I am looking for strategies using semi-definite programming to estimate the error bound of the distilled framework."]}, "idea_from": [255872, 48497], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nWith the advent of big data, we often face the challenge of complex heterogeneity within a dataset. In order to keep user privacy, we would try to avoid sending user data directly to the cloud. Instead, we want a system working on personal devices that can model complex heterogeneity locally. We need a heterogeneous learning framework, which combines both the weighted unsupervised contrastive loss and the weighted supervised contrastive loss to model multiple types of heterogeneity. The framework should also include model distillation techniques such as pruning. Since we need to run the framework on personal devices like smartphones, the framework should provide some guarantee on performance after distillation. I am looking for strategies using semi-definite programming to estimate the error bound of the distilled framework."}


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

{"aspects": {"122": ["449", "269"], "1395": ["449", "117"], "334": ["449", "512", "483"], "1394": ["449", "158"], "333": ["449", "158"]}, "sentence_to_aspect_id": {"I aim to develop a reinforcement learning framework that utilizes human feedback as sparse reward signals.": ["122"], "This framework will have the capacity to incorporate any model for a variety of potential downstream tasks, such as a generative model for art creation or a chatbot for customer service.": ["1395"], "The unique aspect of my framework is its ability to apply reinforcement learning to adjust and optimize the behavior of any downstream model based on various forms of human feedback, such as user satisfaction or the model's adherence to ethical standards.": ["334"], "I understand that obtaining a large amount of human feedback data may be challenging, so my framework needs to be capable of learning from sparse and limited human feedback data.": ["1394"], "I am interested in finding out if there has been any previous work in this area.": [], "Furthermore, I would like to know if a reinforcement learning algorithm would still function effectively when the supervised human feedback data is scarce.": ["333"]}, "aspect_id_to_sentence": {"122": ["I aim to develop a reinforcement learning framework that utilizes human feedback as sparse reward signals."], "1395": ["This framework will have the capacity to incorporate any model for a variety of potential downstream tasks, such as a generative model for art creation or a chatbot for customer service."], "334": ["The unique aspect of my framework is its ability to apply reinforcement learning to adjust and optimize the behavior of any downstream model based on various forms of human feedback, such as user satisfaction or the model's adherence to ethical standards."], "1394": ["I understand that obtaining a large amount of human feedback data may be challenging, so my framework needs to be capable of learning from sparse and limited human feedback data."], "333": ["Furthermore, I would like to know if a reinforcement learning algorithm would still function effectively when the supervised human feedback data is scarce."]}, "idea_from": [279883, 91396, 244786], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI aim to develop a reinforcement learning framework that utilizes human feedback as sparse reward signals. This framework will have the capacity to incorporate any model for a variety of potential downstream tasks, such as a generative model for art creation or a chatbot for customer service. The unique aspect of my framework is its ability to apply reinforcement learning to adjust and optimize the behavior of any downstream model based on various forms of human feedback, such as user satisfaction or the model's adherence to ethical standards. I understand that obtaining a large amount of human feedback data may be challenging, so my framework needs to be capable of learning from sparse and limited human feedback data. I am interested in finding out if there has been any previous work in this area. Furthermore, I would like to know if a reinforcement learning algorithm would still function effectively when the supervised human feedback data is scarce."}


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

{"aspects": {"318": ["681", "568", "668"], "24": ["681", "668", "699"], "495": ["681", "260", "773", "450"], "183": [], "1391": ["681", "670", "659"], "622": []}, "sentence_to_aspect_id": {"I want a model to produce a prediction distribution closely approximating a delta function centered at a single value, but  I also need to make sure the optimal prediction distribution is not an actual delta function.": ["318"], "If I use mean squared loss, the optimal distribution will tend to the delta function.": ["24"], "So I need to address this issue by using a different loss.": ["24"], "I am thinking about using generative models that can sample its output.": ["495"], "Maybe rejection sampling?": ["495"], "This problem might be related to mode collapses and how to avoid them, so I would like to explore that as well.": ["183"], "In addition, the model should not use too many parameters and I want to keep everything in low dimensional spaces.": ["1391"], "Finally, to test if my model works, I want to test on a small image dataset.": ["622"], "In this setting, the delta function would be the salient feature or event in each image.": ["318", "622"]}, "aspect_id_to_sentence": {"318": ["I want a model to produce a prediction distribution closely approximating a delta function centered at a single value, but  I also need to make sure the optimal prediction distribution is not an actual delta function.", "In this setting, the delta function would be the salient feature or event in each image."], "24": ["If I use mean squared loss, the optimal distribution will tend to the delta function.", "So I need to address this issue by using a different loss."], "495": ["I am thinking about using generative models that can sample its output.", "Maybe rejection sampling?"], "183": ["This problem might be related to mode collapses and how to avoid them, so I would like to explore that as well."], "1391": ["In addition, the model should not use too many parameters and I want to keep everything in low dimensional spaces."], "622": ["Finally, to test if my model works, I want to test on a small image dataset.", "In this setting, the delta function would be the salient feature or event in each image."]}, "idea_from": [199764], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI want a model to produce a prediction distribution closely approximating a delta function centered at a single value, but  I also need to make sure the optimal prediction distribution is not an actual delta function. If I use mean squared loss, the optimal distribution will tend to the delta function. So I need to address this issue by using a different loss. I am thinking about using generative models that can sample its output. Maybe rejection sampling? This problem might be related to mode collapses and how to avoid them, so I would like to explore that as well. In addition, the model should not use too many parameters and I want to keep everything in low dimensional spaces. Finally, to test if my model works, I want to test on a small image dataset. In this setting, the delta function would be the salient feature or event in each image."}


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

{"aspects": {"1268": ["783", "778", "1398", "1360"], "1371": [], "1322": [], "812": ["778", "758"], "1181": ["934", "1160", "1269"], "1219": []}, "sentence_to_aspect_id": {"In machine translation tasks, when the source and target languages contain sentences of vastly different levels of complexity, many neural machine translation models do not perform as well.": ["1268"], "For example, if I want to translate very technical medical documents in English to more colloquial and simpler explanations in Swahili, current translation models do not work well.": ["1268", "1371"], "To address this issue, I am looking to develop tools that can simplify complex sentences in a parallel fashion.": ["1322"], "I also want to develop a neural machine translation tool that considers the difference in text complexity levels between the source and target language of a translation corpus.": ["812"], "Of course, since we cannot humanly annotate the level of difficulty for each sentence, I only want to look at unsupervised approaches.": ["1181"], "I am also interested in creating a large-scale pseudo dataset using an unsupervised approach, where I make sure the aligned sentences preserve the same meanings and sentence pairs with a higher complexity difference are kept.": ["1181"], "I hope this pseudo data could improve the performance of my models.": ["1219"], "What would be a good way to model sentence complexity in an unsupervised setting and what would be a good way to create such a pseudo dataset?": ["1181", "1219"]}, "aspect_id_to_sentence": {"1268": ["In machine translation tasks, when the source and target languages contain sentences of vastly different levels of complexity, many neural machine translation models do not perform as well.", "For example, if I want to translate very technical medical documents in English to more colloquial and simpler explanations in Swahili, current translation models do not work well."], "1371": ["For example, if I want to translate very technical medical documents in English to more colloquial and simpler explanations in Swahili, current translation models do not work well."], "1322": ["To address this issue, I am looking to develop tools that can simplify complex sentences in a parallel fashion."], "812": ["I also want to develop a neural machine translation tool that considers the difference in text complexity levels between the source and target language of a translation corpus."], "1181": ["Of course, since we cannot humanly annotate the level of difficulty for each sentence, I only want to look at unsupervised approaches.", "I am also interested in creating a large-scale pseudo dataset using an unsupervised approach, where I make sure the aligned sentences preserve the same meanings and sentence pairs with a higher complexity difference are kept.", "What would be a good way to model sentence complexity in an unsupervised setting and what would be a good way to create such a pseudo dataset?"], "1219": ["I hope this pseudo data could improve the performance of my models.", "What would be a good way to model sentence complexity in an unsupervised setting and what would be a good way to create such a pseudo dataset?"]}, "idea_from": [26460], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn machine translation tasks, when the source and target languages contain sentences of vastly different levels of complexity, many neural machine translation models do not perform as well. For example, if I want to translate very technical medical documents in English to more colloquial and simpler explanations in Swahili, current translation models do not work well. To address this issue, I am looking to develop tools that can simplify complex sentences in a parallel fashion. I also want to develop a neural machine translation tool that considers the difference in text complexity levels between the source and target language of a translation corpus. Of course, since we cannot humanly annotate the level of difficulty for each sentence, I only want to look at unsupervised approaches. I am also interested in creating a large-scale pseudo dataset using an unsupervised approach, where I make sure the aligned sentences preserve the same meanings and sentence pairs with a higher complexity difference are kept. I hope this pseudo data could improve the performance of my models. What would be a good way to model sentence complexity in an unsupervised setting and what would be a good way to create such a pseudo dataset?"}


I am currently involved in a project that aims to improve the safety and efficiency of self-driving cars by predicting the paths of surrounding objects in urban driving situations. Current methods, such as multimodal regression and occupancy maps, have shown limitations in accurately predicting an object’s path. My plan is to treat trajectory prediction as a ranking task, sorting through the set of all potential paths. I need algorithms that can generate a diverse set of potential paths, accommodating a wide range of situations while concurrently discarding paths that are physically unfeasible. To enhance this method, I plan to generate these path sets based on the vehicle's current situation. For example, when the vehicle is on a highway, the model should not generate paths for objects on the roadside. I am also interested in integrating real-time traffic data and weather conditions into the model to increase its accuracy. Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models.

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

{"aspects": {"82": ["476", "91"], "491": ["564", "476"], "222": ["550", "572", "528"], "1390": ["476", "600"], "147": ["476", "148"], "448": [], "478": []}, "sentence_to_aspect_id": {"I am currently involved in a project that aims to improve the safety and efficiency of self-driving cars by predicting the paths of surrounding objects in urban driving situations.": ["82"], "Current methods, such as multimodal regression and occupancy maps, have shown limitations in accurately predicting an object\u2019s path.": ["82"], "My plan is to treat trajectory prediction as a ranking task, sorting through the set of all potential paths.": ["491"], "I need algorithms that can generate a diverse set of potential paths, accommodating a wide range of situations while concurrently discarding paths that are physically unfeasible.": ["222"], "To enhance this method, I plan to generate these path sets based on the vehicle's current situation.": ["1390"], "For example, when the vehicle is on a highway, the model should not generate paths for objects on the roadside.": ["1390"], "I am also interested in integrating real-time traffic data and weather conditions into the model to increase its accuracy.": ["147"], "Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models.": ["448", "478"]}, "aspect_id_to_sentence": {"82": ["I am currently involved in a project that aims to improve the safety and efficiency of self-driving cars by predicting the paths of surrounding objects in urban driving situations.", "Current methods, such as multimodal regression and occupancy maps, have shown limitations in accurately predicting an object\u2019s path."], "491": ["My plan is to treat trajectory prediction as a ranking task, sorting through the set of all potential paths."], "222": ["I need algorithms that can generate a diverse set of potential paths, accommodating a wide range of situations while concurrently discarding paths that are physically unfeasible."], "1390": ["To enhance this method, I plan to generate these path sets based on the vehicle's current situation.", "For example, when the vehicle is on a highway, the model should not generate paths for objects on the roadside."], "147": ["I am also interested in integrating real-time traffic data and weather conditions into the model to increase its accuracy."], "448": ["Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models."], "478": ["Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models."]}, "idea_from": [154695], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am currently involved in a project that aims to improve the safety and efficiency of self-driving cars by predicting the paths of surrounding objects in urban driving situations. Current methods, such as multimodal regression and occupancy maps, have shown limitations in accurately predicting an object\u2019s path. My plan is to treat trajectory prediction as a ranking task, sorting through the set of all potential paths. I need algorithms that can generate a diverse set of potential paths, accommodating a wide range of situations while concurrently discarding paths that are physically unfeasible. To enhance this method, I plan to generate these path sets based on the vehicle's current situation. For example, when the vehicle is on a highway, the model should not generate paths for objects on the roadside. I am also interested in integrating real-time traffic data and weather conditions into the model to increase its accuracy. Lastly, I aim to validate my novel approach using real-world autonomous vehicle data and compare its performance with existing trajectory prediction models."}


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

{"aspects": {"1036": ["781", "1244", "1214", "1052"], "1007": ["781", "1243", "1324"], "933": [], "1008": ["1324", "936", "1320"], "1325": ["1324", "883", "838"], "861": ["862", "1333"]}, "sentence_to_aspect_id": {"I aim to enhance the expressive power of Graph Neural Networks (GNNs) to differentiate between graphs that are structurally different but possess the same local neighborhood structure, a task they traditionally struggle with.": ["1036"], "To achieve this, I plan to develop structure-related features that can aid GNNs in representing any set of nodes, thereby increasing their expressive power.": ["1007"], "Could you suggest some standard graph-distance measures, such as the shortest path distance, that could be used to capture the distance between the node set and each individual node in the graph?": ["933"], "Furthermore, I want these features to enhance the ability of GNN nodes to gather information from neighboring nodes for self-updating.": ["1008"], "Importantly, these features should leverage the sparse structure of the underlying graph, ensuring computational efficiency and scalability.": ["1325"], "Finally, I intend to demonstrate the advantages of my proposed features by testing them on standard network tasks, such as structural role prediction.": ["861"], "Could you also suggest other tasks where these features could be tested?": ["861"]}, "aspect_id_to_sentence": {"1036": ["I aim to enhance the expressive power of Graph Neural Networks (GNNs) to differentiate between graphs that are structurally different but possess the same local neighborhood structure, a task they traditionally struggle with."], "1007": ["To achieve this, I plan to develop structure-related features that can aid GNNs in representing any set of nodes, thereby increasing their expressive power."], "933": ["Could you suggest some standard graph-distance measures, such as the shortest path distance, that could be used to capture the distance between the node set and each individual node in the graph?"], "1008": ["Furthermore, I want these features to enhance the ability of GNN nodes to gather information from neighboring nodes for self-updating."], "1325": ["Importantly, these features should leverage the sparse structure of the underlying graph, ensuring computational efficiency and scalability."], "861": ["Finally, I intend to demonstrate the advantages of my proposed features by testing them on standard network tasks, such as structural role prediction.", "Could you also suggest other tasks where these features could be tested?"]}, "idea_from": [103085], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI aim to enhance the expressive power of Graph Neural Networks (GNNs) to differentiate between graphs that are structurally different but possess the same local neighborhood structure, a task they traditionally struggle with. To achieve this, I plan to develop structure-related features that can aid GNNs in representing any set of nodes, thereby increasing their expressive power. Could you suggest some standard graph-distance measures, such as the shortest path distance, that could be used to capture the distance between the node set and each individual node in the graph? Furthermore, I want these features to enhance the ability of GNN nodes to gather information from neighboring nodes for self-updating. Importantly, these features should leverage the sparse structure of the underlying graph, ensuring computational efficiency and scalability. Finally, I intend to demonstrate the advantages of my proposed features by testing them on standard network tasks, such as structural role prediction. Could you also suggest other tasks where these features could be tested?"}


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

{"aspects": {"1201": ["1019", "844", "1341"], "885": ["1019", "844", "873", "800"], "517": ["848", "1014"], "835": ["1019", "834"], "1037": []}, "sentence_to_aspect_id": {"I have a basic understanding of a research direction in Question & Answering (Q&A) systems.": ["1201"], "These systems employ an integrated knowledge base, structured similarly to a database, with relational dependencies among various concepts and pre-existing answers.": ["1201"], "They interpret user queries and retrieve answers by leveraging the dependencies between concept nodes within the knowledge base.": ["1201"], "I am interested in employing a deep neural network to model this type of knowledge base, thereby eliminating the need for hard-coded, rule-based relationships between concepts.": ["1201"], "I am curious to see if such a neural network model could overcome the limitations inherent in rule-based knowledge base models.": ["885"], "This is because all relationships and dependencies would have implicit vectorized representations, potentially enhancing computational efficiency.": ["517"], "This approach might also augment the model's expressiveness and capacity to answer more complex questions.": ["835"], "My goal is to demonstrate, using Q&A datasets, that my proposed model outperforms traditional Q&A models in terms of increased accuracy and faster runtime.": ["1037"]}, "aspect_id_to_sentence": {"1201": ["I have a basic understanding of a research direction in Question & Answering (Q&A) systems.", "These systems employ an integrated knowledge base, structured similarly to a database, with relational dependencies among various concepts and pre-existing answers.", "They interpret user queries and retrieve answers by leveraging the dependencies between concept nodes within the knowledge base.", "I am interested in employing a deep neural network to model this type of knowledge base, thereby eliminating the need for hard-coded, rule-based relationships between concepts."], "885": ["I am curious to see if such a neural network model could overcome the limitations inherent in rule-based knowledge base models."], "517": ["This is because all relationships and dependencies would have implicit vectorized representations, potentially enhancing computational efficiency."], "835": ["This approach might also augment the model's expressiveness and capacity to answer more complex questions."], "1037": ["My goal is to demonstrate, using Q&A datasets, that my proposed model outperforms traditional Q&A models in terms of increased accuracy and faster runtime."]}, "idea_from": [294361], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI have a basic understanding of a research direction in Question & Answering (Q&A) systems. These systems employ an integrated knowledge base, structured similarly to a database, with relational dependencies among various concepts and pre-existing answers. They interpret user queries and retrieve answers by leveraging the dependencies between concept nodes within the knowledge base. I am interested in employing a deep neural network to model this type of knowledge base, thereby eliminating the need for hard-coded, rule-based relationships between concepts. I am curious to see if such a neural network model could overcome the limitations inherent in rule-based knowledge base models. This is because all relationships and dependencies would have implicit vectorized representations, potentially enhancing computational efficiency. This approach might also augment the model's expressiveness and capacity to answer more complex questions. My goal is to demonstrate, using Q&A datasets, that my proposed model outperforms traditional Q&A models in terms of increased accuracy and faster runtime."}


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

{"aspects": {"935": ["1003", "1198"], "1068": ["1069", "1070"], "968": [], "1218": [], "1216": [], "1235": []}, "sentence_to_aspect_id": {"In the field of robotic manipulation, I have consistently grappled with the challenge of deploying robots in unfamiliar and evolving environments.": ["935"], "These environments could range from outdoor settings to busy traffic roads or even battlefields.": ["935"], "My objective is to enable my robot to operate efficiently in these new surroundings, acquiring and applying new knowledge to better adapt to the environment.": ["1068"], "My current approach is based on the premise that a diverse environment offers a variety of rewards.": ["968"], "Consequently, my robotic agent should be capable of learning to optimize a diverse set of rewards.": ["1218"], "Moreover, the agent should be able to swiftly adapt when the rewards (or objectives) change, especially when the environment remains constant.": ["1216"], "Ultimately, this system should empower robots to learn from and adapt to changes in their environment and tasks.": ["1216"], "This would involve incrementally constructing models based on their own experiences, thereby enhancing their overall performance in real-world scenarios.": ["1235"]}, "aspect_id_to_sentence": {"935": ["In the field of robotic manipulation, I have consistently grappled with the challenge of deploying robots in unfamiliar and evolving environments.", "These environments could range from outdoor settings to busy traffic roads or even battlefields."], "1068": ["My objective is to enable my robot to operate efficiently in these new surroundings, acquiring and applying new knowledge to better adapt to the environment."], "968": ["My current approach is based on the premise that a diverse environment offers a variety of rewards."], "1218": ["Consequently, my robotic agent should be capable of learning to optimize a diverse set of rewards."], "1216": ["Moreover, the agent should be able to swiftly adapt when the rewards (or objectives) change, especially when the environment remains constant.", "Ultimately, this system should empower robots to learn from and adapt to changes in their environment and tasks."], "1235": ["This would involve incrementally constructing models based on their own experiences, thereby enhancing their overall performance in real-world scenarios."]}, "idea_from": [237929], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn the field of robotic manipulation, I have consistently grappled with the challenge of deploying robots in unfamiliar and evolving environments. These environments could range from outdoor settings to busy traffic roads or even battlefields. My objective is to enable my robot to operate efficiently in these new surroundings, acquiring and applying new knowledge to better adapt to the environment. My current approach is based on the premise that a diverse environment offers a variety of rewards. Consequently, my robotic agent should be capable of learning to optimize a diverse set of rewards. Moreover, the agent should be able to swiftly adapt when the rewards (or objectives) change, especially when the environment remains constant. Ultimately, this system should empower robots to learn from and adapt to changes in their environment and tasks. This would involve incrementally constructing models based on their own experiences, thereby enhancing their overall performance in real-world scenarios."}


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

{"aspects": {"363": ["365", "436"], "504": ["364", "569", "709"], "505": ["364", "685", "607"], "41": ["364", "492", "186", "207"], "388": ["258", "259"]}, "sentence_to_aspect_id": {"In our security company, we often deal with low-resolution images of faces captured from surveillance cameras, which makes it difficult to identify individuals accurately.": ["363"], "We are looking for a solution that can generate high-resolution face images from these low-resolution inputs, especially when the input resolution is extremely low (e.g., only several hundred pixels) and the images are captured under uncontrolled settings with large pose and illumination variations.": ["363"], "We hope the solution is based on a deep learning framework that can efficiently utilize global and local constraints defining a face.": ["504"], "It would be beneficial if the framework had the following 2-step processes: one for holistic face reconstruction according to global constraints and another for enhancing face-specific details and enforcing local patch statistics.": ["505"], "Additionally, we would like the deep network to be optimized using a loss function for super-resolution that combines reconstruction error with a learned face quality measured in an adversarial setting, resulting in robust visual results.": ["41"], "We are interested in a solution that has been extensively tested in both controlled and uncontrolled setups and has demonstrated improvements over the current state of the art.": ["388"]}, "aspect_id_to_sentence": {"363": ["In our security company, we often deal with low-resolution images of faces captured from surveillance cameras, which makes it difficult to identify individuals accurately.", "We are looking for a solution that can generate high-resolution face images from these low-resolution inputs, especially when the input resolution is extremely low (e.g., only several hundred pixels) and the images are captured under uncontrolled settings with large pose and illumination variations."], "504": ["We hope the solution is based on a deep learning framework that can efficiently utilize global and local constraints defining a face."], "505": ["It would be beneficial if the framework had the following 2-step processes: one for holistic face reconstruction according to global constraints and another for enhancing face-specific details and enforcing local patch statistics."], "41": ["Additionally, we would like the deep network to be optimized using a loss function for super-resolution that combines reconstruction error with a learned face quality measured in an adversarial setting, resulting in robust visual results."], "388": ["We are interested in a solution that has been extensively tested in both controlled and uncontrolled setups and has demonstrated improvements over the current state of the art."]}, "idea_from": [296033], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn our security company, we often deal with low-resolution images of faces captured from surveillance cameras, which makes it difficult to identify individuals accurately. We are looking for a solution that can generate high-resolution face images from these low-resolution inputs, especially when the input resolution is extremely low (e.g., only several hundred pixels) and the images are captured under uncontrolled settings with large pose and illumination variations. We hope the solution is based on a deep learning framework that can efficiently utilize global and local constraints defining a face. It would be beneficial if the framework had the following 2-step processes: one for holistic face reconstruction according to global constraints and another for enhancing face-specific details and enforcing local patch statistics. Additionally, we would like the deep network to be optimized using a loss function for super-resolution that combines reconstruction error with a learned face quality measured in an adversarial setting, resulting in robust visual results. We are interested in a solution that has been extensively tested in both controlled and uncontrolled setups and has demonstrated improvements over the current state of the art."}


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

{"aspects": {"360": ["67", "563"], "575": ["563", "619"], "549": [], "656": [], "177": [], "1459": ["563", "690"], "1460": ["554", "696"], "599": []}, "sentence_to_aspect_id": {"Our team is developing street views for a mapping application, and we need to censor any potentially sensitive text information within the images we capture.": ["360"], "As such, we require a model capable of detecting and interpreting multi-oriented text in various scenes.": ["575"], "Traditional methods are proving too slow to process the volume of images we capture for the street view.": [], "We think the most efficient solution would be to use two independent models.": ["549", "656"], "The first model should be able to extract text regions from the original image, while the second should interpret the text from the region, regardless of its orientation.": ["549", "656"], "We are seeking a suitable structure of models that can identify text from images, and ideally, these structures should support parallel training due to the large training set we have for this task.": ["177", "1459"], "The second model should also be capable of understanding different languages and local slang.": ["1460"], "Additionally, we would prefer models that come with commercially licensed code.": ["599"]}, "aspect_id_to_sentence": {"360": ["Our team is developing street views for a mapping application, and we need to censor any potentially sensitive text information within the images we capture."], "575": ["As such, we require a model capable of detecting and interpreting multi-oriented text in various scenes."], "549": ["We think the most efficient solution would be to use two independent models.", "The first model should be able to extract text regions from the original image, while the second should interpret the text from the region, regardless of its orientation."], "656": ["We think the most efficient solution would be to use two independent models.", "The first model should be able to extract text regions from the original image, while the second should interpret the text from the region, regardless of its orientation."], "177": ["We are seeking a suitable structure of models that can identify text from images, and ideally, these structures should support parallel training due to the large training set we have for this task."], "1459": ["We are seeking a suitable structure of models that can identify text from images, and ideally, these structures should support parallel training due to the large training set we have for this task."], "1460": ["The second model should also be capable of understanding different languages and local slang."], "599": ["Additionally, we would prefer models that come with commercially licensed code."]}, "idea_from": [166479], "num_aspects": 8, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nOur team is developing street views for a mapping application, and we need to censor any potentially sensitive text information within the images we capture. As such, we require a model capable of detecting and interpreting multi-oriented text in various scenes. Traditional methods are proving too slow to process the volume of images we capture for the street view. We think the most efficient solution would be to use two independent models. The first model should be able to extract text regions from the original image, while the second should interpret the text from the region, regardless of its orientation. We are seeking a suitable structure of models that can identify text from images, and ideally, these structures should support parallel training due to the large training set we have for this task. The second model should also be capable of understanding different languages and local slang. Additionally, we would prefer models that come with commercially licensed code."}


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

{"aspects": {"172": ["84", "150", "443"], "184": ["84", "150", "376"], "156": ["84", "157", "434"], "251": ["96", "252", "650"], "289": ["84", "96", "500", "168"], "182": ["84", "96", "169"], "127": []}, "sentence_to_aspect_id": {"As a researcher focusing on long text summarization, I am keen to understand the key features that contribute to a high-quality summary.": ["172"], "Which metrics would be most useful in assessing the quality of a summary?": ["172"], "I intend to explore the correlation between the quality of summarization and certain NLP features, such as word diversity, entropy, and perplexity.": ["184"], "I am curious to know if similar studies have been conducted previously.": [], "Furthermore, I am interested in examining self-supervised alignment techniques that could assist in aligning the semantic content of a summary with its original text.": ["156"], "My plan is to develop a deep summarization model and an alignment model.": ["251"], "The alignment model, potentially using an attention mechanism, could help align summaries with longer texts.": ["251"], "The quality of this alignment could act as reward signals indicating the quality of the summaries, thereby driving further enhancements in my summarization model.": ["289"], "The improved summarization model could, in turn, enhance the quality of the alignment model.": ["182"], "This process would be iterative.": ["127"]}, "aspect_id_to_sentence": {"172": ["As a researcher focusing on long text summarization, I am keen to understand the key features that contribute to a high-quality summary.", "Which metrics would be most useful in assessing the quality of a summary?"], "184": ["I intend to explore the correlation between the quality of summarization and certain NLP features, such as word diversity, entropy, and perplexity."], "156": ["Furthermore, I am interested in examining self-supervised alignment techniques that could assist in aligning the semantic content of a summary with its original text."], "251": ["My plan is to develop a deep summarization model and an alignment model."], "289": ["The quality of this alignment could act as reward signals indicating the quality of the summaries, thereby driving further enhancements in my summarization model."], "182": ["The improved summarization model could, in turn, enhance the quality of the alignment model."], "127": ["This process would be iterative."]}, "idea_from": [334490, 154484, 345105], "num_aspects": 7, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nAs a researcher focusing on long text summarization, I am keen to understand the key features that contribute to a high-quality summary. Which metrics would be most useful in assessing the quality of a summary? I intend to explore the correlation between the quality of summarization and certain NLP features, such as word diversity, entropy, and perplexity. I am curious to know if similar studies have been conducted previously. Furthermore, I am interested in examining self-supervised alignment techniques that could assist in aligning the semantic content of a summary with its original text. My plan is to develop a deep summarization model and an alignment model. The alignment model, potentially using an attention mechanism, could help align summaries with longer texts. The quality of this alignment could act as reward signals indicating the quality of the summaries, thereby driving further enhancements in my summarization model. The improved summarization model could, in turn, enhance the quality of the alignment model. This process would be iterative."}


In the healthcare industry, keeping the patients’ privacy is critical. Since the amount of data is so large, I need an efficient AI system that can address ethical concerns and uses a machine learning paradigm to identify the private information of the patients and provide robust safeguard to sensitive patient data. The system must be able to maintain privacy protection under large amounts of data. I am looking for an open-sourced model that uses a similar framework like Open Health so that I do not need to spend extra time teaching the staff to use a brand-new system. I heard federated learning is quite popular these days, but it might be hard to find a model using federated learning in the healthcare field since it is very new, so either models using federated learning that is in another field or models does not use federated learning but can effectively protect patient’s privacy work for me.

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

{"aspects": {"1300": ["1017", "1375"], "929": ["930", "1386", "1090"], "1152": [], "1087": ["1088", "744"], "1206": ["1207", "1017", "1375"]}, "sentence_to_aspect_id": {"In the healthcare industry, keeping the patients\u2019 privacy is critical.": ["1300"], "Since the amount of data is so large, I need an efficient AI system that can address ethical concerns and uses a machine learning paradigm to identify the private information of the patients and provide robust safeguard to sensitive patient data.": ["929"], "The system must be able to maintain privacy protection under large amounts of data.": ["1152"], "I am looking for an open-sourced model that uses a similar framework like Open Health so that I do not need to spend extra time teaching the staff to use a brand-new system.": ["1087"], "I heard federated learning is quite popular these days, but it might be hard to find a model using federated learning in the healthcare field since it is very new, so either models using federated learning that is in another field or models does not use federated learning but can effectively protect patient\u2019s privacy work for me.": ["1206"]}, "aspect_id_to_sentence": {"1300": ["In the healthcare industry, keeping the patients\u2019 privacy is critical."], "929": ["Since the amount of data is so large, I need an efficient AI system that can address ethical concerns and uses a machine learning paradigm to identify the private information of the patients and provide robust safeguard to sensitive patient data."], "1152": ["The system must be able to maintain privacy protection under large amounts of data."], "1087": ["I am looking for an open-sourced model that uses a similar framework like Open Health so that I do not need to spend extra time teaching the staff to use a brand-new system."], "1206": ["I heard federated learning is quite popular these days, but it might be hard to find a model using federated learning in the healthcare field since it is very new, so either models using federated learning that is in another field or models does not use federated learning but can effectively protect patient\u2019s privacy work for me."]}, "idea_from": [27798], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nIn the healthcare industry, keeping the patients\u2019 privacy is critical. Since the amount of data is so large, I need an efficient AI system that can address ethical concerns and uses a machine learning paradigm to identify the private information of the patients and provide robust safeguard to sensitive patient data. The system must be able to maintain privacy protection under large amounts of data. I am looking for an open-sourced model that uses a similar framework like Open Health so that I do not need to spend extra time teaching the staff to use a brand-new system. I heard federated learning is quite popular these days, but it might be hard to find a model using federated learning in the healthcare field since it is very new, so either models using federated learning that is in another field or models does not use federated learning but can effectively protect patient\u2019s privacy work for me."}


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

{"aspects": {"293": ["71", "688", "278"], "428": [], "214": ["585", "558", "536"], "85": [], "179": [], "366": []}, "sentence_to_aspect_id": {"I am in the process of developing a deep learning model designed to solve a range of tasks, each with its unique structure.": ["293"], "I am operating under the assumption that the structures of these various tasks can be represented by graphs.": ["293"], "Additionally, I am working with the constraint of having limited labeled data for each task.": ["428"], "My objective is twofold.": [], "Firstly, I aim to create a model capable of processing graphical data, learning from diverse tasks, and then adapting to new tasks using a few-shot learning approach.": ["214"], "My current strategy involves the use of a graph meta-learning algorithm.": ["214", "85"], "Secondly, I have observed that when a sub-task is adversarially designed, it can severely disrupt the meta-learning algorithm.": ["179"], "As a result, I need to ensure my model is robust against adversarial attacks.": ["179"], "My current strategy to achieve this involves introducing randomized variations in the model architecture, input data, and intermediate latent results to fortify my model.": ["366"], "I am also open to other robust training strategies.": ["179"], "By integrating these techniques, my goal is to develop a scalable and robust deep learning model that can efficiently adapt to new graph-based tasks and resist adversarial attacks.": ["179"]}, "aspect_id_to_sentence": {"293": ["I am in the process of developing a deep learning model designed to solve a range of tasks, each with its unique structure.", "I am operating under the assumption that the structures of these various tasks can be represented by graphs."], "428": ["Additionally, I am working with the constraint of having limited labeled data for each task."], "214": ["Firstly, I aim to create a model capable of processing graphical data, learning from diverse tasks, and then adapting to new tasks using a few-shot learning approach.", "My current strategy involves the use of a graph meta-learning algorithm."], "85": ["My current strategy involves the use of a graph meta-learning algorithm."], "179": ["Secondly, I have observed that when a sub-task is adversarially designed, it can severely disrupt the meta-learning algorithm.", "As a result, I need to ensure my model is robust against adversarial attacks.", "I am also open to other robust training strategies.", "By integrating these techniques, my goal is to develop a scalable and robust deep learning model that can efficiently adapt to new graph-based tasks and resist adversarial attacks."], "366": ["My current strategy to achieve this involves introducing randomized variations in the model architecture, input data, and intermediate latent results to fortify my model."]}, "idea_from": [7228, 118766, 192890], "num_aspects": 6, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nI am in the process of developing a deep learning model designed to solve a range of tasks, each with its unique structure. I am operating under the assumption that the structures of these various tasks can be represented by graphs. Additionally, I am working with the constraint of having limited labeled data for each task. My objective is twofold. Firstly, I aim to create a model capable of processing graphical data, learning from diverse tasks, and then adapting to new tasks using a few-shot learning approach. My current strategy involves the use of a graph meta-learning algorithm. Secondly, I have observed that when a sub-task is adversarially designed, it can severely disrupt the meta-learning algorithm. As a result, I need to ensure my model is robust against adversarial attacks. My current strategy to achieve this involves introducing randomized variations in the model architecture, input data, and intermediate latent results to fortify my model. I am also open to other robust training strategies. By integrating these techniques, my goal is to develop a scalable and robust deep learning model that can efficiently adapt to new graph-based tasks and resist adversarial attacks."}


My current job involves efficient image retrieval. The primary issue is that images require substantial storage space, and the constant conversion to and from compact code results in unnecessary time expenditure. Therefore, I am seeking a model capable of retrieving images in compact code format. The challenge for such a model is that images in compact code do not always maintain close local information, leading to increased computational costs for local features. Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features. Additionally, I require algorithms that can add labels or annotations directly to the images’ compact code without necessitating any format conversion of the image. I prefer methods that have demonstrated effective performance on popular image retrieval benchmarks.

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

{"aspects": {"76": ["144", "617"], "516": ["144", "17"], "518": ["144", "679"], "12": ["13", "617", "669"], "522": ["144", "472"]}, "sentence_to_aspect_id": {"My current job involves efficient image retrieval.": ["76"], "The primary issue is that images require substantial storage space, and the constant conversion to and from compact code results in unnecessary time expenditure.": [], "Therefore, I am seeking a model capable of retrieving images in compact code format.": ["76"], "The challenge for such a model is that images in compact code do not always maintain close local information, leading to increased computational costs for local features.": ["516"], "Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features.": ["516", "518"], "Additionally, I require algorithms that can add labels or annotations directly to the images\u2019 compact code without necessitating any format conversion of the image.": ["12"], "I prefer methods that have demonstrated effective performance on popular image retrieval benchmarks.": ["522"]}, "aspect_id_to_sentence": {"76": ["My current job involves efficient image retrieval.", "Therefore, I am seeking a model capable of retrieving images in compact code format."], "516": ["The challenge for such a model is that images in compact code do not always maintain close local information, leading to increased computational costs for local features.", "Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features."], "518": ["Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features."], "12": ["Additionally, I require algorithms that can add labels or annotations directly to the images\u2019 compact code without necessitating any format conversion of the image."], "522": ["I prefer methods that have demonstrated effective performance on popular image retrieval benchmarks."]}, "idea_from": [241439], "num_aspects": 5, "query_with_instruction": "Given a query, find relevant scientific papers which satisfy the query\nMy current job involves efficient image retrieval. The primary issue is that images require substantial storage space, and the constant conversion to and from compact code results in unnecessary time expenditure. Therefore, I am seeking a model capable of retrieving images in compact code format. The challenge for such a model is that images in compact code do not always maintain close local information, leading to increased computational costs for local features. Consequently, I am interested in image retrieval algorithms and innovative loss functions that prioritize global features. Additionally, I require algorithms that can add labels or annotations directly to the images\u2019 compact code without necessitating any format conversion of the image. I prefer methods that have demonstrated effective performance on popular image retrieval benchmarks."}