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NEW QUESTION # 277
Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?
Answer: B,C,E
Explanation:
ReLU avoids saturation like sigmoid, helping gradients flow. Skip connections provide alternative pathways for gradients. Batch normalization stabilizes learning and can help mitigate vanishing gradients. Increasing learning rate is unrelated, and sigmoid exacerbates the problem due to saturation.
NEW QUESTION # 278
You are building a multi-modal model that combines text and image data for a search application. The goal is to retrieve relevant images given a text query. You have encoded both images and text into embeddings. What's a suitable loss function for training the model to ensure images relevant to a text query are ranked higher than irrelevant ones?
Answer: E
Explanation:
Triplet Loss is specifically designed for ranking tasks. It takes three inputs: an anchor (text query), a positive example (relevant image), and a negative example (irrelevant image). The loss function aims to minimize the distance between the anchor and the positive example while maximizing the distance between the anchor and the negative example. Contrastive loss works with pairs, not relative rankings. Cross-entropy, MSE, and KL Divergence are not suitable for ranking problems.
NEW QUESTION # 279
You have developed a multimodal model that predicts stock prices using news articles (text), historical stock data (time-series), and company financial reports (tabular data). You want to deploy this model using NVIDIA Triton Inference Server. Assume you have preprocessed the data and have individual models for each modality. What is the recommended approach to configure Triton for efficient and scalable multimodal inference?
Answer: B
Explanation:
Using Triton's Ensemble Modeling feature (B) is the most efficient approach. It allows you to define a pipeline that includes preprocessing, individual modality models, and fusion logic within a single Triton model, simplifying deployment and management. This approach optimizes inter-model communication and reduces client-side overhead.
NEW QUESTION # 280
You are working with time-series data from IoT sensors alongside video footage from surveillance cameras to detect anomalies in a factory production line. What data preprocessing steps are crucial for effectively integrating and analyzing these modalities in a multimodal AI model?
Answer: E
Explanation:
All the mentioned steps are crucial. Synchronizing timestamps is essential for temporal alignment. Normalizing time-series data ensures features are on the same scale, preventing bias. Downsampling video reduces computational burden, and grayscale conversion simplifies feature extraction without losing vital information for anomaly detection.
NEW QUESTION # 281
You are working with a multimodal dataset containing images and corresponding text descriptions. You want to train a model to generate text descriptions for new images. You decide to use a transformer-based architecture with separate encoders for images and text. How should you effectively fuse the image and text representations to enable cross-modal interaction?
Answer: C
Explanation:
Cross-attention allows the decoder to selectively attend to relevant parts of both the image and text representations, enabling fine- grained interaction between the modalities. Concatenation or averaging simply combines the representations without allowing for selective attention. Training the encoders separately and then combining their outputs doesn't allow for cross modal interaction during training. Multiply operation is not standard and is not efficient.
NEW QUESTION # 282
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