mteb/BIRCO-DorisMae-Test huggingface dataset:
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. 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.
As a healthcare organization, our goal is to create a system that can effectively extract crucial information from Electronic Health Records (EHRs) to enhance patient care and medical research. Given the challenge of limited labeled data in EHRs, we intend to utilize transfer learning and cross-lingual methods to train a model for this task. Consequently, we are interested in identifying tasks that bear the highest similarity to information extraction from EHRs. Due to privacy concerns, we are unable to test the model on actual EHRs. Therefore, we require methods to generate datasets that closely resemble our format, using fictitious patient information, to test our model. Furthermore, since some EHRs may not convert doctors’ notes into text, the model should be capable of interpreting the doctor's handwriting and accurately extracting information from these notes. We have a preference for pre-trained models that have been benchmarked or models that come with training codes.
I am currently developing a transparent and reliable AI chatbot. I am in search of AI models that can complete incomplete sentences or answer questions based on the next token prediction. For transparency, I need models that can explicitly compute the probability of the next token and update it in real-time based on user input. I also aim to provide a visualization that displays the perceptrons and weights utilized during each inference to the user. To ensure trustworthiness, I require techniques that can perturb the training set to increase the model's resilience against adversarial attacks. The model should also possess the ability to forget or replace outdated knowledge, as some data from the training set may become incorrect over time. Considering that a significant portion of potential training data from the internet includes sensitive personal information, I also require an efficient filter to automatically mask such information prior to its use in training. Ultimately, my objective is to develop an AI chatbot that is both transparent and trustworthy.
I aim to assist actors in better comprehending their roles in movies or TV shows by creating a system that can comprehend and analyze the mood and personality of characters within a narrative context. My plan is to utilize transformer-based models in conjunction with a reinforcement learning (RL) framework to accomplish this. The transformer-based model should be capable of inferring from the context and dialogue to predict a character's potential mood and anticipate their next line. The RL framework will then compare the predicted conversation with the actual conversation in the reward formula to provide weak self-supervision during the training process. I am aware of the concept of a warm start in training, but I am unsure how to adapt this to my current project. Furthermore, the target narrative contexts, such as scripts, differ from typical texts, so I would appreciate advice on the appropriate length for the context window in this type of text. Finally, the model should be able to map mood or personality to facial expressions or character appearances to provide guidance to the actors.
I aim to create a chatbot capable of interpreting images as input and analyzing them for subsequent tasks. The model should deliver responses efficiently without compromising the quality of the answers. To achieve this, I require an image analysis model that can swiftly perform image classification by ignoring unnecessary local detailed features. Additionally, the model should be capable of executing pixel-level localization and analysis as required. To enhance efficiency, I plan to incorporate reinforcement learning for user prediction. I envision the chatbot predicting the user's potential next questions, allowing the image analysis model to analyze relevant images while awaiting user input. The reward component of the framework should be directly linked to the accuracy of the prediction and the time saved. Furthermore, I want the method to remember the most frequently asked questions related to popular images, enabling it to provide immediate answers to these queries.
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. 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.
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. 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.
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? 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.
I 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.
As an environmental researcher, I aim to create a mobile application that uses generative adversarial networks (GANs) to produce personalized, vivid images illustrating the potential impacts of climate change on specific locations. My plan is to train the GAN on street-view images of horses before and after extreme weather events. However, since such a dataset does not exist, I require a model capable of learning the 3D structure of an object from images taken from various angles and subsequently rendering an image of the structure from a specified direction. Furthermore, I intend for the application to incorporate climate models to evaluate the probability and type of long-term climate-related events. As I am developing a mobile application, I also need techniques to reduce the latency between the mobile device and the backend server, as well as strategies to manage multiple simultaneous calls to the backend server. Lastly, I am interested in video compression algorithms that can enhance the upload speed and alleviate the storage pressure on the server.
In the context of time series forecasting, it's observed that anomalies in certain data sources, such as seismic activity, heart rate monitors, and nuclear plant cooling system temperatures, occur infrequently but often result in catastrophic outcomes like earthquakes, heart attacks, and nuclear meltdowns. My objective is to devise a time series forecasting model capable of predicting these rare anomalies with high precision. This model must be equipped to manage extremely sparse supervised signals. To train my model effectively, I believe it's necessary to employ some form of sampling or bootstrapping to simulate various event pathways leading to these anomalies. Essentially, this involves data augmentation to generate more positive signals for training. Consequently, I require a simulation agent capable of learning the underlying time series data distribution. This simulation agent (model) must also be generative to create new pathways and simulate new events. I propose that the simulation agent should incorporate a reinforcement learning component. However, I am open to any literature that discusses rare event simulation, regardless of whether the method is statistical or deep learning-based. Ultimately, I am confident that my model will be robust enough for deployment in real-world scenarios for disaster prevention and early warning systems.